From e10ffaf39fc0522ab18d0e917a71e6613e52059d Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Tue, 7 Jul 2026 13:33:08 +0530 Subject: [PATCH 01/42] docs(evaluation): Overview revamp Co-Authored-By: Claude Opus 4.8 (1M context) --- src/pages/docs/evaluation/index.mdx | 62 ++++++++++++++++------------- 1 file changed, 35 insertions(+), 27 deletions(-) diff --git a/src/pages/docs/evaluation/index.mdx b/src/pages/docs/evaluation/index.mdx index dd526cb4..8a244893 100644 --- a/src/pages/docs/evaluation/index.mdx +++ b/src/pages/docs/evaluation/index.mdx @@ -1,45 +1,53 @@ --- -title: "Future AGI Evaluation: Measure Prompt and Agent Quality" -description: "Measure and compare the quality of prompts and agents across datasets, simulations, and experiments using built-in or custom eval templates." +title: "Overview" +description: "Evaluations measure how well your AI achieves the user's goal, scored on metrics like accuracy, safety, and tone." --- -## About +Evaluations measure how well your AI achieves the user's goal, scored on a metric like task completion, factual accuracy, safety, or tone. You define what "good" means once, and Future AGI scores every response against it, returning a result and a plain-language reason for each one. -Evaluation is Future AGI's quality measurement layer. It gives you a consistent, repeatable way to measure whether your prompts and agents are behaving correctly and whether changes you make improve things or introduce regressions. +Evaluation is the middle of a loop, not a standalone tool: you observe what your agent did, evaluate its quality, optimize the prompt or model, and enforce a bar before anything ships. The same eval runs everywhere your work lives, so a score means the same thing on a dataset, a live trace, or a pull request. -There are two building blocks: **eval templates** define what to measure (task completion, tone, hallucination, safety, factual accuracy, or a custom rule you write yourself), and **eval configs** define how to measure (the judge model, input mapping, and run settings). Combine them with your data and you get a score, a pass/fail result, and an optional explanation per row or call, plus aggregated summaries, KPIs, and trend data across runs. +Four objects carry the whole product: a [template](/docs/evaluation/concepts/eval-templates) defines what to measure, a config points it at your data, a run executes, and a [score](/docs/evaluation/concepts/eval-results) comes out. Future AGI ships 156 built-in evals, or you write your own. -Evaluations run across every surface in Future AGI: datasets, simulations, experiments, playground, replay sessions, and CI/CD pipelines. You can also run them programmatically via the SDK. Using the same templates and configs across contexts keeps results directly comparable without redefining your quality criteria each time. +## Start here -Future AGI ships 70+ built-in templates covering quality, safety, factuality, RAG retrieval, format, bias, audio, and image evaluation. You can also create custom templates and bundle any combination into **eval groups** to apply multiple evals in a single run. - -## How Evaluation Connects to Other Features + + + Every surface you can run an eval on, and how to get going + + + 156 ready evaluators across quality, safety, RAG, format, and more + + -- **Datasets**: Run evals across dataset rows and store scores as new columns. [Learn more](/docs/dataset) -- **Simulation**: Score simulated agent conversations for quality, context retention, and escalation. [Learn more](/docs/simulation) -- **Optimization**: Feed eval results into prompt optimization to improve quality automatically. [Learn more](/docs/optimization) -- **CI/CD**: Gate pull requests on eval scores to catch regressions before they ship. [Learn more](/docs/evaluation/features/cicd) -- **Error Feed**: Eval-powered scoring for every traced agent execution. [Learn more](/docs/error-feed) +## Understand the model -## Getting Started +A few short pages give you the whole mental model behind Evaluations: - - Run the first eval from the UI or SDK in minutes. + + The template, config, run, and score model, and the quality loop - - 70+ templates: quality, safety, factuality, RAG, and more. + + Agent Evaluator, LLM-as-Judge, and Code Eval, and when to reach for each - - Define your own eval rules and output types. + + What decides the score, and how to choose one - - Bundle multiple evals and run them together. + + The value, optional reason, and aggregates you get back - - Pick the right evaluation model for your task. + + +## How it connects + +Evaluation sits in the middle of Future AGI and feeds on the rest: [Observe](/docs/observe) turns live traces into eval inputs and shows scores back on the trace, datasets and simulations are where offline evals run, optimization consumes scores to improve a prompt, and thresholds become production gates. + + + + Attach an eval to a project's traces and score new data as it arrives - - Run evals automatically on every pull request. + + Turn an eval threshold into a merge gate that blocks regressions From 1e445d231886cec60e49c297c447d26aa599968e Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Tue, 7 Jul 2026 13:33:15 +0530 Subject: [PATCH 02/42] docs(evaluation): Understanding Evaluation concept Co-Authored-By: Claude Opus 4.8 (1M context) --- .../concepts/understanding-evaluation.mdx | 76 ++++++++----------- 1 file changed, 33 insertions(+), 43 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx b/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx index c09f74ce..1b416179 100644 --- a/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx +++ b/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx @@ -1,58 +1,48 @@ --- -title: "Understanding AI Evaluation in Future AGI Platform" -description: "Covers how evaluation works in Future AGI: templates, judge models, eval types, results, and where evaluations run in the platform and SDK." +title: "Understanding Evaluation" +description: "The four objects behind every eval: template, config, run, and score, and the quality loop they sit in." --- -## About +Evaluation is Future AGI's quality measurement layer. You define what "good" means once, and the platform scores every response against that definition, returning a result and a plain-language reason for each one. Because the same definition runs everywhere your work lives, a score means the same thing on a dataset, a simulation, a live trace, or a pull request. -Evaluation in Future AGI is a systematic way to measure whether your AI is producing the right outputs. You define what "right" means once, using an eval template, and the platform scores every response automatically against that definition, returning a result and a reason for each one. +## The four objects -Every eval run has three components working together: a **template** that defines the criteria, a **judge model** that applies the criteria to each response, and a **result** that records the outcome. You supply the data; the platform handles the scoring. +Four objects carry the whole product. Learn these and the rest is detail: ---- - -## How it works - -1. **Choose a template**: Select a built-in template (e.g. Toxicity, Groundedness, Tone) or create a custom one with your own rule prompt. Templates define what to measure and what output type to expect (pass/fail, score, or a category). - -2. **Map your data**: Tell the eval which columns or input keys contain the text to evaluate (e.g. which column is the model response, which is the reference context). - -3. **Pick a judge model**: Choose a Future AGI model (e.g. `turing_flash`) or bring your own via a custom model integration. The judge reads each row and applies the template criteria. - -4. **Run**: The platform processes every row in parallel. Each row gets a result (pass/fail or a score) and a reason explaining the judgment. +- A **template** defines *what* to measure: the criteria, the expected output type, and a pass threshold. Templates are reusable and versioned, and are either built by Future AGI or written by you +- A **config** defines *how* to measure for one run: which [judge model](/docs/evaluation/concepts/judge-models), how your data maps to the template's inputs, and the run settings +- A **run** is one execution: a template plus a config plus one unit of data, from a single row to millions +- A **score** is the outcome: a value (pass or fail, a number, or a category), an optional reason, and the metadata to trace it back -5. **Review**: Results appear as new columns in your dataset, or as aggregated summaries and KPIs across runs. +Read it left to right: a template is the definition, a config points it at data, a run executes, and a score comes out. Everything else in Evaluations elaborates on one of these four. ---- - -## Where evals run - -Evals are not limited to datasets. The same templates work across every surface in Future AGI: +## The quality loop -| Surface | What you evaluate | -|---|---| -| **Dataset** | Score every row in a dataset against one or more templates | -| **Simulation** | Evaluate agent responses in a run test against your criteria | -| **Experiments** | Compare prompt or model versions using the same eval criteria | -| **CI/CD pipeline** | Run evals automatically on every code change and track results by version | -| **SDK** | Call `evaluator.evaluate()` from any script or application | +Evaluation is the middle of a loop, not a standalone tool. You [observe](/docs/observe) what your agent did, evaluate its quality, optimize the prompt or model, and enforce a bar before anything ships. Traces become eval inputs, scores drive optimization, and thresholds become production gates. -Using the same template across surfaces keeps results directly comparable without redefining criteria each time. +## How an eval reaches a verdict ---- - -## Key concepts +Every eval is authored as one of three [types](/docs/evaluation/concepts/eval-types): -- **[Eval templates](/docs/evaluation/concepts/eval-templates)**: The definition of what to measure. Built-in or custom. -- **[Judge models](/docs/evaluation/concepts/judge-models)**: The model that applies the template criteria and produces the result. -- **[Eval results](/docs/evaluation/concepts/eval-results)**: The output of a run: result value, reason, and aggregates. -- **[Eval groups](/docs/evaluation)**: Named collections of templates you run together as a single unit. +- **Agent Evaluator** reasons over multiple turns and can use tools to reach a judgment +- **LLM-as-Judge** has a judge model read the response and apply the criteria in one pass +- **Code Eval** computes the result in a sandbox, with no model and no API key ---- +The type decides how the verdict is reached and whether a judge model is involved. -## Next steps +## Keep exploring -- [Evaluate via Platform & SDK](/docs/evaluation/features/evaluate): Run your first eval. -- [Built-in evals](/docs/evaluation/builtin): 70+ templates across quality, safety, factuality, RAG, and more. -- [Create custom evals](/docs/evaluation/features/custom): Define your own criteria and rule prompts. -- [Eval groups](/docs/evaluation): Bundle multiple evals and run them in one pass. + + + Agent Evaluator, LLM-as-Judge, and Code Eval in depth + + + Criteria, inputs, output types, and version snapshots + + + The models that read and score a response + + + The value, optional reason, and aggregates + + From 465cbcdd3a3818a02109317f76433f94f18c8ab0 Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Tue, 7 Jul 2026 13:33:23 +0530 Subject: [PATCH 03/42] docs(evaluation): Eval types (three types) Co-Authored-By: Claude Opus 4.8 (1M context) --- .../docs/evaluation/concepts/eval-types.mdx | 143 +++++------------- 1 file changed, 37 insertions(+), 106 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/eval-types.mdx b/src/pages/docs/evaluation/concepts/eval-types.mdx index c00c3a98..419fcaba 100644 --- a/src/pages/docs/evaluation/concepts/eval-types.mdx +++ b/src/pages/docs/evaluation/concepts/eval-types.mdx @@ -1,122 +1,53 @@ --- -title: "Eval Types: Four Evaluation Methods in Future AGI" -description: "The four evaluation methods in Future AGI: LLM as Judge, Deterministic, Statistical Metric, and LLM as Ranker, and how modality affects which ones apply." +title: "Eval types" +description: "The three eval types: Agent Evaluator, LLM-as-Judge, and Code Eval, and when to reach for each." --- -## About +Every eval is authored as one of three types. The type decides how the eval reaches its verdict, what it can reason over, and whether a [judge model](/docs/evaluation/concepts/judge-models) is involved. It is the first choice you make when you create an eval. -Every eval template in Future AGI uses one of four evaluation methods to produce a result. The method determines how the eval computes its output, whether a judge model is required, and what kind of result to expect. Choosing the right type for your use case gives you the right balance of accuracy, speed, and cost. +## Agent Evaluator ---- - -## LLM as Judge - -The judge model reads the response, applies the template's criteria, and reasons about whether it passes. This is the most flexible type: it handles subjective, context-dependent, and nuanced quality checks that cannot be expressed as a fixed rule. - -**Requires a judge model.** Configure one in [Future AGI models](/docs/evaluation/features/futureagi-models) or [custom models](/docs/evaluation/features/custom-models). - -**Returns**: a result (pass/fail, score, or category) and a plain-language **reason** explaining the judgment. - -**Examples**: Groundedness, Toxicity, Task Completion, Tone, Detect Hallucination, Instruction Adherence, PII Detection, Context Adherence, and all custom evals. - -**Best for:** -- Quality checks that require understanding meaning or intent -- Safety and policy enforcement -- RAG pipeline evaluation (context adherence, relevance, chunk attribution) -- Custom business or regulatory rules written in plain language - ---- +A reasoning evaluator that runs multiple turns and can use tools and knowledge bases to reach a judgment. Reach for it when a single prompt cannot capture the check, like scoring a multi-step agent transcript or a judgment that needs to look something up. It is the most capable type, and the newest. -## Deterministic / Rule-based +Requires a judge model. -Computed directly from the text using code or string logic. No model is called and no API key is required. Given the same input, always returns the same output. +## LLM-as-Judge -**Does not require a judge model.** Runs locally; works without an API key via the standalone `evaluate()` function. +A judge model reads the response, applies the template's criteria in a single pass, and returns a result plus a reason. It is the workhorse for subjective, context-dependent quality: safety, tone, faithfulness, instruction adherence, and any custom rule you can write in plain language. -**Returns**: pass/fail only. No reason field. +Requires a judge model. -**Examples**: Is JSON, Is Email, Contains Valid Link, No Invalid Links, One Line. +## Code Eval -**Best for:** -- Format validation (valid JSON, email address, URL presence) -- High-volume pipelines where speed and zero API cost matter -- Offline or air-gapped environments -- First-pass filtering before running LLM-based evals +Deterministic logic runs in a sandbox (Python or JavaScript) and computes the result directly from the text. Given the same input it always returns the same output, calls no model, and needs no API key. This is the type behind format validation (valid JSON, email, URL), overlap and similarity metrics (BLEU, ROUGE, edit distance, embedding similarity), and retrieval metrics (recall@k, precision@k, NDCG, MRR). ---- - -## Statistical Metric - -Computes a numeric score using an algorithm applied to the output and a reference value. Covers overlap metrics, edit distance, semantic similarity, and information retrieval metrics. No judge model is needed for most: embedding-based metrics call an embedding model, not a generative one. - -**Returns**: a numeric score (e.g. 0–1 or 0–100). No reason field. - -**Examples:** - -| Metric | What it measures | -|---|---| -| BLEU, ROUGE | N-gram overlap between output and reference | -| Levenshtein Similarity | Character edit distance between output and reference | -| Numeric Similarity | Numerical difference between output and reference | -| Embedding Similarity | Semantic vector similarity between output and reference | -| Semantic List Contains | Whether output contains phrases semantically similar to a reference list | -| Recall@K, Precision@K, NDCG@K, MRR, Hit Rate | Retrieval quality for RAG pipelines | -| FID Score | Distribution similarity between sets of real and generated images | -| CLIP Score | Alignment between an image and its text description | - -**Best for:** -- Benchmarking against a ground-truth reference answer -- RAG retrieval quality (recall, precision, ranking) -- Image generation quality -- Reproducible, model-free scoring - ---- +No judge model, and it runs locally. -## LLM as Ranker +## Which type to reach for -A variant of LLM as Judge where instead of scoring a single response, the model ranks a set of retrieved context chunks based on relevance to a query. Used specifically for evaluating retrieval ordering in RAG pipelines. +- **Code Eval** when the check is objective and computable +- **LLM-as-Judge** when it needs an understanding of meaning +- **Agent Evaluator** when one pass is not enough -**Requires a judge model.** - -**Returns**: a ranked score per context item. - -**Examples**: Eval Ranking. - -**Best for:** -- Evaluating whether a retrieval system surfaces the most relevant chunks at the top -- Diagnosing retrieval ordering issues in RAG pipelines - ---- - -## Modality - -In addition to the four types above, evals also vary by the kind of input they accept: - -| Modality | What it evaluates | Example evals | -|---|---|---| -| **Text** | Any text input or output | Most built-in evals | -| **Image** | Images passed as inputs | CLIP Score, FID Score, Caption Hallucination, Image Instruction Adherence, Synthetic Image Evaluator, OCR Evaluation | -| **Audio** | Audio files or speech | Audio Quality, Audio Transcription, TTS Accuracy | -| **Conversation** | Multi-turn conversation histories | Customer Agent evals (Loop Detection, Context Retention, Query Handling, etc.) | - -Multimodal evals (image, audio, conversation) require a judge model that supports the relevant modality. Use `turing_large` or `turing_small` for image and audio inputs. - ---- - -## Quick reference - -| Type | Judge model required | Returns reason | No API key possible | +| Type | Judge model | Returns a reason | Runs without an API key | |---|---|---|---| -| LLM as Judge | Yes | Yes | No | -| Deterministic | No | No | Yes | -| Statistical Metric | No (most) | No | Yes (most) | -| LLM as Ranker | Yes | No | No | - ---- - -## Next steps - -- [Built-in evals](/docs/evaluation/builtin): Full list with evaluation method and required inputs for each template. -- [Create custom evals](/docs/evaluation/features/custom): Custom evals always use LLM as Judge. -- [Judge models](/docs/evaluation/concepts/judge-models): Choose the right model for LLM as Judge and LLM as Ranker evals. -- [Eval groups](/docs/evaluation): Combine different eval types and run them together in one pass. +| Agent Evaluator | Yes | Yes | No | +| LLM-as-Judge | Yes | Yes | No | +| Code Eval | No | No | Yes | + +## Keep exploring + + + + The models Agent Evaluator and LLM-as-Judge use + + + Where the criteria and output type live + + + 156 evaluators, each tagged with its type + + + Write your own, usually as LLM-as-Judge + + From a6263226319817df5b71448584c76f73173782bb Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Tue, 7 Jul 2026 15:31:40 +0530 Subject: [PATCH 04/42] docs(evaluation): repurpose Evaluate page into the Running evaluations hub TH-6511 --- .../docs/evaluation/features/evaluate.mdx | 179 +++--------------- 1 file changed, 22 insertions(+), 157 deletions(-) diff --git a/src/pages/docs/evaluation/features/evaluate.mdx b/src/pages/docs/evaluation/features/evaluate.mdx index 57a3ffe4..3348afca 100644 --- a/src/pages/docs/evaluation/features/evaluate.mdx +++ b/src/pages/docs/evaluation/features/evaluate.mdx @@ -1,170 +1,35 @@ --- -title: "Evaluate via Platform and SDK in Future AGI" -description: "Run evaluations using the Future AGI platform UI or the Python SDK. Choose individual templates or batch runs for scalable model assessment." +title: "Running evaluations on Future AGI" +description: "Where to run evals on Future AGI, and the guide for each surface." --- -## About +Evaluations have no separate quickstart. An eval scores data, so you run it wherever your data already lives: a dataset, your live traces, a simulation, the playground, the SDK, or a CI job. The template you run is the same on every surface. The surface only changes what you feed it and where the score lands. -Evaluation is how you measure whether your AI is actually doing what you want it to do. +This page is the entry point. Pick your surface below and follow its guide. -You give it an input (a prompt, a response, a conversation) and an eval scores it. The score tells you if the output was accurate, safe, on-topic, well-structured, or whatever quality you care about. Every evaluation returns a **result** (e.g. Passed / Failed, or a numeric score), and a **reason** explaining why. +## Where evals run -You can run evaluations two ways: +| Surface | Where you run it | Guide | +|---|---|---| +| Dataset and experiments | Offline, over every row of a dataset | [Run experiments](/docs/dataset/features/experiments) | +| Traces | Online, on live spans, traces, and sessions | [Set up evals in Observe](/docs/observe/guides/setup-evals) | +| Simulation | Over simulated conversations | [Run a simulation](/docs/simulation/features/run-simulation) | +| Playground | On one sample input, to see the verdict and reason before you commit | In product | +| SDK | Programmatic runs, with local Code Evals that need no API key | [Evaluation SDK](/docs/sdk/evals) | +| CI/CD | On every pull request, against a stored baseline | [Evaluate in CI/CD](/docs/evaluation/features/cicd) | -- **Platform UI**: point-and-click on a dataset. No code required. -- **Python SDK**: call `evaluator.evaluate()` from your code, scripts, or CI pipeline. +The online how-to for scoring live traces lives in Observe; this hub only points to it. Everything else runs from its own module, with the same templates and the same scores. -Both support the same [built-in eval templates](/docs/evaluation/builtin) (e.g. Toxicity, Groundedness, Tone) and any custom evals you've defined. +## Dive deeper ---- - -## When to use - -- **Catch regressions before they ship**: Run evals in CI so a bad prompt change or model update gets flagged before it reaches production. -- **Score outputs at scale**: Attach evals to a dataset and every row gets a score automatically, without reviewing each one manually. -- **Enforce safety and compliance**: Check every response for toxicity, PII, bias, or data privacy issues as part of your standard pipeline. -- **Compare models or prompts**: Evaluate the same inputs across different models or prompt variations to see which performs better on your criteria. -- **Monitor quality over time**: Run the same evals repeatedly to track whether your AI's output quality is improving or degrading. - ---- - -## How to - -Choose **UI** or **SDK** below; each tab has the process in steps. - - - - - - You need a dataset to run evals from the UI. If you don’t have one, add a dataset first. See [Dataset overview](/docs/dataset). - ![Select a dataset](/screenshot/product/evaluation/evaluate/1.png) - - - Open your dataset, then click **Evaluate** in the top-right. The evaluation configuration panel opens. - ![Open the evaluation panel](/screenshot/product/evaluation/evaluate/2.png) - - - Click **Add Evaluation**. Choose a **built-in template** (e.g. Tone) or click **Create your own eval**. For a built-in template: click it, give it a **name**, and under **config** select the **dataset column(s)** to use as input (and output if required). - ![Add and run an eval](/screenshot/product/evaluation/evaluate/3.png) - ![Add and run an eval](/screenshot/product/evaluation/evaluate/4.png) - - - Optionally enable **Error Localization** to pinpoint which part of a row caused a failure. Select a **model** if the template requires one. Click **Add & Run** to score every row in the dataset. - ![Add and run an eval](/screenshot/product/evaluation/evaluate/5.png) - - - From the Add Evaluation flow, click **Create your own eval** to define a custom template (name, model, rule prompt, output type, and optional settings). After you save it, the new eval appears in the evaluation list and you can add it to your dataset as in the step above. For full details on creating and configuring custom evals, see [Create custom evals](/docs/evaluation/features/custom). - - - - - - Some evals can run without an API key using the standalone `evaluate()` function, including local metrics like `contains`, `faithfulness`, and LLM-as-judge. See the SDK reference for details. - - - - Install the package **ai-evaluation** and create an `Evaluator` with your Future AGI API key and secret. Prefer setting **FI_API_KEY** and **FI_SECRET_KEY** in the environment instead of passing them in code. See [Accessing API keys](/docs/admin-settings#accessing-api-keys). - - ```bash - pip install ai-evaluation - ``` - - ```python - from fi.evals import Evaluator - - evaluator = Evaluator( - fi_api_key="your_api_key", - fi_secret_key="your_secret_key", - ) - ``` - - - Call `evaluate` with the eval template **name** (e.g. `tone`), **inputs** (dict with the keys the template expects, e.g. `"input"`), and **model_name**. Many built-in (system) templates require a model. - - ```python - result = evaluator.evaluate( - eval_templates="tone", - inputs={ - "input": "Dear Sir, I hope this email finds you well. I look forward to any insights or advice you might have whenever you have a free moment" - }, - model_name="turing_flash", - ) - print(result.eval_results[0].output) - print(result.eval_results[0].reason) - ``` - - - For long-running or large runs, set `is_async=True`. The call returns immediately with an **eval_id**; the evaluation runs in the background. - - ```python - result = evaluator.evaluate( - eval_templates="tone", - inputs={"input": "Your text here"}, - model_name="turing_flash", - is_async=True, - ) - eval_id = result.eval_results[0].eval_id - ``` - - - Use `get_eval_result(eval_id)` to fetch the result when the evaluation has finished. The same method works for both sync and async runs (e.g. to re-fetch a result). - - ```python - result = evaluator.get_eval_result(eval_id) - print(result.eval_results[0].output) - print(result.eval_results[0].reason) - ``` - - - To use a template you created in the UI, pass its **name** as `eval_templates` and supply the **inputs** dict with the keys your template’s required_keys expect (e.g. `"input"`, `"output"`). Use the same template name you see in the evaluation list. - - ```python - result = evaluator.evaluate( - eval_templates="name-of-your-eval", - inputs={ - "input": "your_input_text", - "output": "your_output_text" - }, - model_name="model_name" - ) - - print(result.eval_results[0].output) - print(result.eval_results[0].reason) - ``` - - - - - For **system (built-in)** eval templates, **model_name** is required and must be one of the models listed for that template. The backend validates required input keys from the template’s config. - - - - - -For more eval templates and Future AGI models, see [Built-in evals](/docs/evaluation/builtin) and [Future AGI models](/docs/evaluation/features/futureagi-models). - - ---- - -## Next Steps - - - - Define your own eval rules and criteria. - - - Run multiple evals together as a group. - - - Bring your own model for evaluations. - - - Built-in models available for evals. + + + Write your own when no built-in fits - - Run evals automatically in your pipeline. + + 156 templates across 14 groups - - How evaluation fits into the platform. + + Pick a judge model for your evals From fa3bd9891967c3826676c3ba8493f8d5b9a55a36 Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Tue, 7 Jul 2026 15:31:40 +0530 Subject: [PATCH 05/42] docs(evaluation): add Composite evals & groups concept TH-6511 --- .../evaluation/concepts/composite-evals.mdx | 57 +++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 src/pages/docs/evaluation/concepts/composite-evals.mdx diff --git a/src/pages/docs/evaluation/concepts/composite-evals.mdx b/src/pages/docs/evaluation/concepts/composite-evals.mdx new file mode 100644 index 00000000..1f05a016 --- /dev/null +++ b/src/pages/docs/evaluation/concepts/composite-evals.mdx @@ -0,0 +1,57 @@ +--- +title: "Composite evals & groups" +description: "How a composite eval rolls several child templates into one score, and how eval groups apply a set of templates together." +--- + +A **composite eval** rolls several child templates into a single score, so one check can stand for several at once. An **eval group** is a named set of templates you apply together, so a whole quality bar travels as one unit. Both let you treat "good" as more than one measurement without wiring the same pieces together by hand every time. + +## Composite evals + +Every template is either `single` or `composite`. A composite template holds an ordered list of **child** templates. When it runs, the parent scores each child on the same input, then combines the child scores into one parent score with an [aggregation function](#aggregation-functions). + +Children share one output type, either pass/fail, score, or choices, so their scores are comparable before they are combined. A composite made of graded children produces a graded score; one made of pass/fail children produces a pass/fail-style score. + +Reach for a composite when "good" is several checks at once, for example faithful *and* on-tone *and* complete. Instead of reading three separate results, you get one score that already reflects all three, and one threshold to gate on. + +## Aggregation functions + +The parent combines child scores with one of five functions. Every child score is normalized to a 0 to 1 range first, so they can be compared on the same scale. + +| Function | What it returns | +|---|---| +| Weighted average | Each child score times its weight, divided by the total weight. This is the default | +| Average | The plain mean of the child scores | +| Minimum | The lowest child score, a strict gate where every child has to do well | +| Maximum | The highest child score | +| Pass rate | The fraction of children that individually meet their own pass threshold | + +Pick the function to the shape of the check. Use minimum when any one failure should sink the whole score, pass rate when you care how many checks passed rather than by how much, and weighted average when some children matter more than others. + +## Weights and pinned versions + +Two settings on each child keep a composite stable as it grows. + +- **Weight** sets how much a child counts in a weighted average, from 0.0 to 10.0, defaulting to 1.0. A child you care about more gets a higher weight, and a child at 0.0 drops out of the weighted score +- **Pinned version** locks a child to a specific [template version](/docs/evaluation/concepts/eval-templates), so the composite keeps scoring the same way even after that child template is edited later + +Weights only change the weighted average function; the other four ignore them. Pinning matters most for a composite you rely on in CI, where a silent change to a child would quietly move the gate. + +## Eval groups + +An **eval group** is a named collection of templates you apply together in a single run. Where a composite folds its children into one combined score, a group runs its templates side by side and reports each result on its own. + +Use a group when a whole suite belongs together, for example a safety bar, a tone bar, and a retrieval bar. You attach the group by its name instead of hand-picking the same templates every time, so the bar stays consistent across datasets, simulations, and traces. + +## Keep exploring + + + + Run a composite or a group on any surface + + + Write a child template of your own + + + 156 templates you can drop in as children + + From cbc437c745a407b2588b8eba135d8d9121bad679 Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Tue, 7 Jul 2026 15:31:40 +0530 Subject: [PATCH 06/42] docs(evaluation): align Create a custom eval to Khushal bar TH-6511 --- src/pages/docs/evaluation/features/custom.mdx | 51 +++++++------------ 1 file changed, 17 insertions(+), 34 deletions(-) diff --git a/src/pages/docs/evaluation/features/custom.mdx b/src/pages/docs/evaluation/features/custom.mdx index 66c648b6..77362a61 100644 --- a/src/pages/docs/evaluation/features/custom.mdx +++ b/src/pages/docs/evaluation/features/custom.mdx @@ -1,28 +1,22 @@ --- -title: "Create Custom Evaluation Templates in Future AGI" -description: "Define custom evaluation criteria and rules tailored to your use case, extending beyond the built-in templates available in Future AGI." +title: "Create a custom eval" +description: "Write your own eval when no built-in template fits." --- -## About +Every AI product has its own definition of a good response. When no [built-in template](/docs/evaluation/builtin) fits, a custom eval lets you encode that definition yourself. You write the criteria once, in plain language, and Future AGI scores every response against it, returning a result and a reason for each one. -Every AI product has its own definition of a good response. Custom evals let you encode those rules and run them at scale, you write the criteria once, in plain language, and Future AGI scores every response against it automatically, returning a result and a reason for each one. - -Once created, a custom eval works exactly like any built-in template: use it on a dataset, in a simulation, or call it from the SDK. - ---- +Most custom evals are [LLM-as-Judge](/docs/evaluation/concepts/eval-types), where a judge model applies your rule in one pass. Use a Code Eval when the rule is objective and computable, or an Agent Evaluator when one pass will not do. Once created, a custom eval works exactly like a built-in one: use it on a dataset, in a simulation, or from the SDK, by its name. ## When to use -- **Domain-specific validation**: Assess content against industry or regulatory standards that aren’t in the default templates. -- **Business rule compliance**: Enforce your organization’s guidelines (tone, format, disclosures) in a repeatable eval. -- **Complex or weighted scoring**: Implement multi-criteria or custom scoring logic via your rule prompt. -- **Custom output formats**: Validate specific response structures or formats (e.g. JSON shape, required fields) with a tailored eval. - ---- +- Assess content against industry or regulatory standards that the default templates do not cover +- Enforce your organization's own guidelines for tone, format, or required disclosures +- Apply multi-criteria or weighted scoring through the rule prompt +- Validate a specific response structure, such as a JSON shape or a set of required fields ## How to -You can create custom evals from the **UI** or via the **SDK** (by calling the REST API from your code). After the template is saved, run it from the UI or from the evaluation SDK using the template name. +You can create a custom eval from the **UI** or via the **SDK** (by calling the REST API from your code). After the template is saved, run it from the UI or from the evaluation SDK using the template name. @@ -57,7 +51,7 @@ You can create custom evals from the **UI** or via the **SDK** (by calling the R Click **Create Evaluation**. The new template appears in your evaluation list and can be added to any dataset or called via the SDK using the name you gave it. - In your dataset, click **Evaluate** → **Add Evaluation**, select the custom eval you created, map the **columns** to the rule-prompt variables, then click **Add & Run**. See [Running your first eval](/docs/evaluation) for the full UI flow. + In your dataset, click **Evaluate** then **Add Evaluation**, select the custom eval you created, map the **columns** to the rule-prompt variables, then click **Add & Run**. See [Running evaluations on Future AGI](/docs/evaluation/features/evaluate) for the full run flow. @@ -123,27 +117,16 @@ You can create custom evals from the **UI** or via the **SDK** (by calling the R ---- - -## Next Steps +## Dive deeper - - - Run evals from the UI or SDK. - - - Add your custom eval to a group and run it with others. + + + Run your custom eval on any surface - Bring your own model for evaluations. - - - Built-in models available for evals. - - - Run evals automatically in your pipeline. + Bring your own judge model - - How evaluation fits into the platform. + + 156 templates you can use instead From 31529b8d126a41a7dbe3a5a30b9bba914c578786 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Wed, 8 Jul 2026 15:33:54 +0530 Subject: [PATCH 07/42] Finalizing sidebar --- src/lib/navigation.ts | 55 ++++++++++++++++++++++++++++++++++--------- 1 file changed, 44 insertions(+), 11 deletions(-) diff --git a/src/lib/navigation.ts b/src/lib/navigation.ts index e9b8315a..f10e75c1 100644 --- a/src/lib/navigation.ts +++ b/src/lib/navigation.ts @@ -295,21 +295,54 @@ export const tabNavigation: NavTab[] = [ title: 'Concepts', items: [ { title: 'Understanding Evaluation', href: '/docs/evaluation/concepts/understanding-evaluation' }, - { title: 'Eval Types', href: '/docs/evaluation/concepts/eval-types' }, - { title: 'Eval Templates', href: '/docs/evaluation/concepts/eval-templates' }, - { title: 'Judge Models', href: '/docs/evaluation/concepts/judge-models' }, - { title: 'Eval Results', href: '/docs/evaluation/concepts/eval-results' }, + { title: 'Eval types', href: '/docs/evaluation/concepts/eval-types' }, + { title: 'Eval templates & versions', href: '/docs/evaluation/concepts/eval-templates' }, + { title: 'Judge models', href: '/docs/evaluation/concepts/judge-models' }, + { title: 'Context', href: '/docs/evaluation/concepts/context' }, + { title: 'Connectors', href: '/docs/evaluation/concepts/connectors' }, + { title: 'Output types', href: '/docs/evaluation/concepts/output-types' }, + { title: 'Error localization', href: '/docs/evaluation/concepts/error-localization' }, + { title: 'Guardrails', href: '/docs/evaluation/concepts/guardrails' }, + { title: 'Composite evals', href: '/docs/evaluation/concepts/composite-evals' }, + { title: 'Feedback', href: '/docs/evaluation/concepts/feedback' }, + { title: 'Ground truth', href: '/docs/evaluation/concepts/ground-truth' }, ] }, { - title: 'Features', + title: 'How to guides', + items: [ + { title: 'Running Evaluations', href: '/docs/evaluation/features/evaluate' }, + { + title: 'Explore playground', + items: [ + { title: 'Overview', href: '/docs/evaluation/guides/explore-playground' }, + { title: 'Test an eval', href: '/docs/evaluation/guides/explore-playground/test-an-eval' }, + { title: 'Usage & analytics', href: '/docs/evaluation/guides/explore-playground/usage-analytics' }, + { title: 'Eval logs', href: '/docs/evaluation/guides/explore-playground/eval-logs' }, + ] + }, + { title: 'Create a custom eval', href: '/docs/evaluation/features/custom' }, + { title: 'Build a composite evals', href: '/docs/evaluation/guides/composite-evals' }, + { title: 'Set up guardrails', href: '/docs/evaluation/guides/guardrails' }, + { title: 'Add ground truth', href: '/docs/evaluation/guides/ground-truth' }, + { title: 'Collect feedback', href: '/docs/evaluation/guides/feedback' }, + { title: 'Configure connectors', href: '/docs/evaluation/guides/connectors' }, + { title: 'Use custom models', href: '/docs/evaluation/features/custom-models' }, + { title: 'Evaluate in CI/CD', href: '/docs/evaluation/features/cicd' }, + ] + }, + { + title: 'References', + items: [ + { title: 'Built-in evals', href: '/docs/evaluation/builtin' }, + { title: 'Output types & scoring', href: '/docs/evaluation/reference/output-types' }, + { title: 'SDK & API', href: '/docs/evaluation/reference/sdk-api' }, + ] + }, + { + title: 'Troubleshooting', items: [ - { title: 'Built-in Evals', href: '/docs/evaluation/builtin' }, - { title: 'Evaluate via Platform & SDK', href: '/docs/evaluation/features/evaluate' }, - { title: 'Create Custom Evals', href: '/docs/evaluation/features/custom' }, - { title: 'Use Custom Models', href: '/docs/evaluation/features/custom-models' }, - { title: 'Future AGI Models', href: '/docs/evaluation/features/futureagi-models' }, - { title: 'Evaluate CI/CD Pipeline', href: '/docs/evaluation/features/cicd' }, + { title: 'Evaluation FAQ & fixes', href: '/docs/evaluation/troubleshooting' }, ] }, ] From 7beb782e9cfe3311ee34b46b480699c406d47bba Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Wed, 8 Jul 2026 15:56:38 +0530 Subject: [PATCH 08/42] docs(evaluation): rename eval-results concept to output-types --- src/pages/docs/evaluation/concepts/eval-templates.mdx | 2 +- src/pages/docs/evaluation/concepts/judge-models.mdx | 2 +- .../concepts/{eval-results.mdx => output-types.mdx} | 4 ++-- .../docs/evaluation/concepts/understanding-evaluation.mdx | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) rename src/pages/docs/evaluation/concepts/{eval-results.mdx => output-types.mdx} (93%) diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index 4201fec8..5e36c291 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -68,4 +68,4 @@ Built-in templates have fixed required keys documented in the template reference - [Create custom evals](/docs/evaluation/features/custom): Write your own criteria and rule prompts. - [Eval types](/docs/evaluation/concepts/eval-types): LLM as Judge, Deterministic, Statistical Metric, and LLM as Ranker. - [Judge models](/docs/evaluation/concepts/judge-models): How the model applies a template to produce a result. -- [Eval results](/docs/evaluation/concepts/eval-results): What the output of an eval run looks like. +- [Output types](/docs/evaluation/concepts/output-types): What the output of an eval run looks like diff --git a/src/pages/docs/evaluation/concepts/judge-models.mdx b/src/pages/docs/evaluation/concepts/judge-models.mdx index 60eacd86..59ecadb3 100644 --- a/src/pages/docs/evaluation/concepts/judge-models.mdx +++ b/src/pages/docs/evaluation/concepts/judge-models.mdx @@ -58,4 +58,4 @@ You can also bring your own model using the [custom models](/docs/evaluation/fea - [Future AGI models](/docs/evaluation/features/futureagi-models): Full reference for built-in judge models. - [Use custom models](/docs/evaluation/features/custom-models): Bring your own model as the judge. - [Eval templates](/docs/evaluation/concepts/eval-templates): The criteria the judge applies. -- [Eval results](/docs/evaluation/concepts/eval-results): What the judge produces after scoring. +- [Output types](/docs/evaluation/concepts/output-types): What the judge produces after scoring diff --git a/src/pages/docs/evaluation/concepts/eval-results.mdx b/src/pages/docs/evaluation/concepts/output-types.mdx similarity index 93% rename from src/pages/docs/evaluation/concepts/eval-results.mdx rename to src/pages/docs/evaluation/concepts/output-types.mdx index d11d1840..47209dbb 100644 --- a/src/pages/docs/evaluation/concepts/eval-results.mdx +++ b/src/pages/docs/evaluation/concepts/output-types.mdx @@ -1,6 +1,6 @@ --- -title: "Eval Results: Reading and Storing Evaluation Outputs" -description: "Understand what evaluation results contain, how to read them, and how results are stored and aggregated across runs in Future AGI." +title: "Output types" +description: "What an eval returns: pass/fail, scores, or choices, and where results land" --- ## About diff --git a/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx b/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx index c09f74ce..71b6fa37 100644 --- a/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx +++ b/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx @@ -45,7 +45,7 @@ Using the same template across surfaces keeps results directly comparable withou - **[Eval templates](/docs/evaluation/concepts/eval-templates)**: The definition of what to measure. Built-in or custom. - **[Judge models](/docs/evaluation/concepts/judge-models)**: The model that applies the template criteria and produces the result. -- **[Eval results](/docs/evaluation/concepts/eval-results)**: The output of a run: result value, reason, and aggregates. +- **[Output types](/docs/evaluation/concepts/output-types)**: The output of a run: result value, reason, and aggregates - **[Eval groups](/docs/evaluation)**: Named collections of templates you run together as a single unit. --- From a84dce325cbf19b5f20fcd226a41c4253a750b45 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Wed, 8 Jul 2026 16:31:08 +0530 Subject: [PATCH 09/42] docs(evaluation): fold futureagi-models into the judge-models concept --- src/lib/redirects.ts | 2 +- .../evaluation/eval-correction-loop.mdx | 2 +- .../docs/evaluation/concepts/eval-types.mdx | 2 +- .../docs/evaluation/concepts/judge-models.mdx | 27 +++--- .../evaluation/features/custom-models.mdx | 2 +- src/pages/docs/evaluation/features/custom.mdx | 4 +- .../docs/evaluation/features/evaluate.mdx | 4 +- .../evaluation/features/futureagi-models.mdx | 95 ------------------- src/pages/docs/evaluation/index.mdx | 2 +- 9 files changed, 24 insertions(+), 116 deletions(-) delete mode 100644 src/pages/docs/evaluation/features/futureagi-models.mdx diff --git a/src/lib/redirects.ts b/src/lib/redirects.ts index 430aee47..e08d5883 100644 --- a/src/lib/redirects.ts +++ b/src/lib/redirects.ts @@ -157,7 +157,7 @@ export const redirectMap: Record = { '/future-agi/get-started/evaluation/create-custom-evals': '/docs/evaluation/features/custom', '/future-agi/get-started/evaluation/evaluate-ci-cd-pipeline': '/docs/evaluation/features/cicd', '/future-agi/get-started/evaluation/evaluate-patterns': '/docs/evaluation/features/evaluate', - '/future-agi/get-started/evaluation/future-agi-models': '/docs/evaluation/features/futureagi-models', + '/future-agi/get-started/evaluation/future-agi-models': '/docs/evaluation/concepts/judge-models', '/future-agi/get-started/evaluation/running-your-first-eval': '/docs/evaluation/features/evaluate', '/future-agi/get-started/evaluation/use-custom-models': '/docs/evaluation/features/custom-models', '/future-agi/get-started/knowledge-base/concept': '/docs/knowledge-base/concepts/concept', diff --git a/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx b/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx index 4141f59e..b2f46c78 100644 --- a/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx +++ b/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx @@ -253,7 +253,7 @@ You ran a built-in eval, found rows where it disagreed with human judgment, enco Full reference for the custom eval template API - + Pick the right judge model: turing_small, turing_flash, turing_large diff --git a/src/pages/docs/evaluation/concepts/eval-types.mdx b/src/pages/docs/evaluation/concepts/eval-types.mdx index c00c3a98..4ffc74bb 100644 --- a/src/pages/docs/evaluation/concepts/eval-types.mdx +++ b/src/pages/docs/evaluation/concepts/eval-types.mdx @@ -13,7 +13,7 @@ Every eval template in Future AGI uses one of four evaluation methods to produce The judge model reads the response, applies the template's criteria, and reasons about whether it passes. This is the most flexible type: it handles subjective, context-dependent, and nuanced quality checks that cannot be expressed as a fixed rule. -**Requires a judge model.** Configure one in [Future AGI models](/docs/evaluation/features/futureagi-models) or [custom models](/docs/evaluation/features/custom-models). +**Requires a judge model.** Configure one in [Future AGI models](/docs/evaluation/concepts/judge-models) or [custom models](/docs/evaluation/features/custom-models). **Returns**: a result (pass/fail, score, or category) and a plain-language **reason** explaining the judgment. diff --git a/src/pages/docs/evaluation/concepts/judge-models.mdx b/src/pages/docs/evaluation/concepts/judge-models.mdx index 59ecadb3..c42940c3 100644 --- a/src/pages/docs/evaluation/concepts/judge-models.mdx +++ b/src/pages/docs/evaluation/concepts/judge-models.mdx @@ -24,17 +24,21 @@ The judge model does not generate or modify your AI's responses. It only reads a ## Available judge models -Future AGI provides a set of proprietary models built specifically for evaluation: - -| Model | Code | Best for | Latency | -|---|---|---|---| -| TURING_LARGE | `turing_large` | Max accuracy, multimodal evals (text, image, audio) | Higher | -| TURING_SMALL | `turing_small` | High fidelity at lower cost (text, image) | Medium | -| TURING_FLASH | `turing_flash` | Fast, high-accuracy evals (text, image) | Low | -| PROTECT | `protect` | Safety, guardrails, user-defined rules (text, audio) | Low | -| PROTECT_FLASH | `protect_flash` | First-pass binary filtering (text only) | Ultra-low | - -See [Future AGI models](/docs/evaluation/features/futureagi-models) for full details on each model. +Future AGI provides a set of proprietary models built specifically for evaluation, not general-purpose chat or generation: + +- **TURING_LARGE** `turing_large`: Flagship evaluation model that delivers best-in-class accuracy across multimodal inputs (text, images, audio). Recommended when maximal precision outweighs latency constraints +- **TURING_SMALL** `turing_small`: Compact variant that preserves high evaluation fidelity while lowering computational cost. Supports text and image evaluations +- **TURING_FLASH** `turing_flash`: Latency-optimised version of TURING, providing high-accuracy assessments for text and image inputs with fast response times +- **PROTECT** `protect`: Real-time guardrailing model for safety, policy compliance, and content-risk detection. Offers very low latency on text and audio streams and permits user-defined rule sets +- **PROTECT_FLASH** `protect_flash`: Ultra-fast binary guardrail for text content. Designed for first-pass filtering where millisecond-level turnaround is critical + +| Model | Code | Inputs | Best for | Latency | +| --- | --- | --- | --- | --- | +| TURING_LARGE | `turing_large` | Text, image, audio | Max accuracy, multimodal evals | Higher | +| TURING_SMALL | `turing_small` | Text, image | High fidelity, lower cost | Medium | +| TURING_FLASH | `turing_flash` | Text, image | Fast, high-accuracy evals | Low | +| PROTECT | `protect` | Text, audio | Safety, guardrails, user-defined rules | Low | +| PROTECT_FLASH | `protect_flash` | Text | First-pass binary filtering | Ultra-low | You can also bring your own model using the [custom models](/docs/evaluation/features/custom-models) integration. This is useful when you need a domain-specific fine-tuned model, want to keep inference in a specific cloud region, or already pay for a model you want to use as the judge. @@ -55,7 +59,6 @@ You can also bring your own model using the [custom models](/docs/evaluation/fea ## Next steps -- [Future AGI models](/docs/evaluation/features/futureagi-models): Full reference for built-in judge models. - [Use custom models](/docs/evaluation/features/custom-models): Bring your own model as the judge. - [Eval templates](/docs/evaluation/concepts/eval-templates): The criteria the judge applies. - [Output types](/docs/evaluation/concepts/output-types): What the judge produces after scoring diff --git a/src/pages/docs/evaluation/features/custom-models.mdx b/src/pages/docs/evaluation/features/custom-models.mdx index d9d8a4a5..57d28ad0 100644 --- a/src/pages/docs/evaluation/features/custom-models.mdx +++ b/src/pages/docs/evaluation/features/custom-models.mdx @@ -129,7 +129,7 @@ Fields you may see when adding a model (from a provider or custom). **Applies to Run multiple evals together as a group. - + Built-in models available for evals. diff --git a/src/pages/docs/evaluation/features/custom.mdx b/src/pages/docs/evaluation/features/custom.mdx index 66c648b6..0b541eb3 100644 --- a/src/pages/docs/evaluation/features/custom.mdx +++ b/src/pages/docs/evaluation/features/custom.mdx @@ -34,7 +34,7 @@ You can create custom evals from the **UI** or via the **SDK** (by calling the R **Name**: unique name for the eval (lowercase letters, numbers, hyphens, and underscores only; cannot start or end with `-` or `_`). Used when you add the eval to a dataset or call it from the SDK. - **Model**: choose **Use Future AGI Models** (e.g. turing_large, turing_flash, turing_small, protect, protect_flash) or **Use other LLMs** (your own or external providers). For model details, see [Future AGI models](/docs/evaluation/features/futureagi-models) and [Use custom models](/docs/evaluation/features/custom-models). + **Model**: choose **Use Future AGI Models** (e.g. turing_large, turing_flash, turing_small, protect, protect_flash) or **Use other LLMs** (your own or external providers). For model details, see [Future AGI models](/docs/evaluation/concepts/judge-models) and [Use custom models](/docs/evaluation/features/custom-models). ![Configure basic settings](/screenshot/product/evaluation/create-custom-evals/2.png) @@ -137,7 +137,7 @@ You can create custom evals from the **UI** or via the **SDK** (by calling the R Bring your own model for evaluations. - + Built-in models available for evals. diff --git a/src/pages/docs/evaluation/features/evaluate.mdx b/src/pages/docs/evaluation/features/evaluate.mdx index 57a3ffe4..f97c711b 100644 --- a/src/pages/docs/evaluation/features/evaluate.mdx +++ b/src/pages/docs/evaluation/features/evaluate.mdx @@ -141,7 +141,7 @@ Choose **UI** or **SDK** below; each tab has the process in steps. -For more eval templates and Future AGI models, see [Built-in evals](/docs/evaluation/builtin) and [Future AGI models](/docs/evaluation/features/futureagi-models). +For more eval templates and Future AGI models, see [Built-in evals](/docs/evaluation/builtin) and [Future AGI models](/docs/evaluation/concepts/judge-models). --- @@ -158,7 +158,7 @@ For more eval templates and Future AGI models, see [Built-in evals](/docs/evalua Bring your own model for evaluations. - + Built-in models available for evals. diff --git a/src/pages/docs/evaluation/features/futureagi-models.mdx b/src/pages/docs/evaluation/features/futureagi-models.mdx deleted file mode 100644 index 7dce3c35..00000000 --- a/src/pages/docs/evaluation/features/futureagi-models.mdx +++ /dev/null @@ -1,95 +0,0 @@ ---- -title: "Use Future AGI Models for AI Evaluation and Scoring" -description: "Future AGI's proprietary judge models are trained on diverse datasets to perform accurate evaluations and score AI outputs." ---- - -## About - -When you run an evaluation, the model you choose determines how accurately and how fast each response gets scored. Future AGI provides a set of proprietary models built and optimized specifically for evaluation, not general-purpose chat or generation. - -Each model is designed for a different need. Some prioritize accuracy across complex multimodal inputs. Others are built for speed, making them suitable for real-time guardrailing or high-volume pipelines. Choosing the right model lets you balance quality and performance for your specific workload. - -All models are available in the platform UI and the SDK, and work with both built-in and custom eval templates. - ---- - -## Available models - -- **TURING_LARGE** `turing_large`: Flagship evaluation model that delivers best-in-class accuracy across multimodal inputs (text, images, audio). Recommended when maximal precision outweighs latency constraints. - -- **TURING_SMALL** `turing_small`: Compact variant that preserves high evaluation fidelity while lowering computational cost. Supports text and image evaluations. - -- **TURING_FLASH** `turing_flash`: Latency-optimised version of TURING, providing high-accuracy assessments for text and image inputs with fast response times. - -- **PROTECT** `protect`: Real-time guardrailing model for safety, policy compliance, and content-risk detection. Offers very low latency on text and audio streams and permits user-defined rule sets. - -- **PROTECT_FLASH** `protect_flash`: Ultra-fast binary guardrail for text content. Designed for first-pass filtering where millisecond-level turnaround is critical. - ---- -### Quick comparison - -| Model | Code | Inputs | Best for | Latency | -| --- | --- | --- | --- | --- | -| TURING_LARGE | `turing_large` | Text, image, audio | Max accuracy, multimodal evals | Higher | -| TURING_SMALL | `turing_small` | Text, image | High fidelity, lower cost | Medium | -| TURING_FLASH | `turing_flash` | Text, image | Fast, high-accuracy evals | Low | -| PROTECT | `protect` | Text, audio | Safety, guardrails, user-defined rules | Low | -| PROTECT_FLASH | `protect_flash` | Text | First-pass binary filtering | Ultra-low | - ---- - -## How to - - - - - - When adding or configuring an evaluation on a dataset or run test, choose **Use Future AGI Models** and pick a model from the dropdown. - ![Use Future AGI Models in the UI](/screenshot/product/evaluation/future-agi-models/1.png) - - - - - - - Pass `model_name` in your `evaluator.evaluate()` call. Use the model code from the table above (e.g. `turing_flash`, `turing_large`, `protect`). - - ```python - from fi.evals import Evaluator - - evaluator = Evaluator(fi_api_key="...", fi_secret_key="...") - result = evaluator.evaluate( - eval_templates="tone", - inputs={"input": "Your text to evaluate."}, - model_name="turing_small", # or turing_flash, turing_large, protect, protect_flash - ) - ``` - - - - - ---- - -## Next Steps - - - - Run evals from the UI or SDK. - - - Define your own eval rules and choose a model to run them. - - - Run multiple evals together as a group. - - - Bring your own model for evaluations. - - - Run evals automatically in your pipeline. - - - How evaluation fits into the platform. - - diff --git a/src/pages/docs/evaluation/index.mdx b/src/pages/docs/evaluation/index.mdx index dd526cb4..a18ea934 100644 --- a/src/pages/docs/evaluation/index.mdx +++ b/src/pages/docs/evaluation/index.mdx @@ -36,7 +36,7 @@ Future AGI ships 70+ built-in templates covering quality, safety, factuality, RA Bundle multiple evals and run them together. - + Pick the right evaluation model for your task. From 750c2f7b4b9019091688fa2f47b46e278d406f64 Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Wed, 8 Jul 2026 18:17:12 +0530 Subject: [PATCH 10/42] docs(evaluation): add Error localization concept page --- .../concepts/error-localization.mdx | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 src/pages/docs/evaluation/concepts/error-localization.mdx diff --git a/src/pages/docs/evaluation/concepts/error-localization.mdx b/src/pages/docs/evaluation/concepts/error-localization.mdx new file mode 100644 index 00000000..383e3fcb --- /dev/null +++ b/src/pages/docs/evaluation/concepts/error-localization.mdx @@ -0,0 +1,66 @@ +--- +title: "Error localization" +description: "Pinpoint which input field caused an evaluation to fail" +--- + +## About + +When an evaluation fails, error localization narrows down the cause by analyzing which input field led to the failure. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible. This helps you debug faster and see whether the problem is in your data, your retrieval, or your instructions. + +--- + +## How it works + +Error localization runs when an evaluation produces a failed result. The process is: + +1. **Evaluation fails**: the eval score falls below the configured threshold, triggering error localization. +2. **Analysis**: an error-localizing agent receives the input data, the eval criteria, the eval result, and the judge's explanation, then works out which input drove the failure. +3. **Result**: you get back an `error_analysis` block describing the problem and a `selected_input_key` naming the input field identified as the issue. +4. **Review**: in the dataset or trace view, the selected field is flagged so you can review it directly. + +The analysis depends on the eval criteria and the judge's explanation, so vague rule prompts produce less precise results. + +--- + +## When error localization runs + +Error localization is selective. It only runs under specific conditions: + +| Condition | What happens | +|---|---| +| Evaluation passes | Skipped; there is no error to locate | +| Evaluation fails | Runs to identify the problem | +| Code-type eval | Skipped; not applicable to code logic | +| Composite eval | Not supported yet | +| Non-scorable result | Skipped if the result cannot be converted to a numeric score | + +--- + +## Where results appear + +Error localization results appear wherever evaluations run: + +- **Datasets**: the `selected_input_key` is shown alongside the eval result, so you can see which field was identified as problematic +- **Observe traces**: when you run an eval on a span, the result appears in the eval log attached to that span +- **SDK**: the `error_analysis` and `selected_input_key` fields are returned in the result object + +--- + +## How to enable it + +Error localization is opt-in on the eval template: + +1. Open an eval template, built-in or custom. +2. Set `error_localizer_enabled` to true on the template. +3. Run an evaluation that uses the template. +4. If a result fails, error localization runs and its output is captured. + +Each eval run inherits the template setting and can override it. + +--- + +## Next steps + +- [Evaluate via Platform and SDK](/docs/evaluation/features/evaluate): Run an eval and see error localization results. +- [Eval templates](/docs/evaluation/concepts/eval-templates): Enable error localization when you define a template. +- [Understanding evaluation](/docs/evaluation/concepts/understanding-evaluation): Learn how evaluations work end-to-end. From 8df8412d7d173404ab8eb2a3f72d176b3cf75e8c Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Wed, 8 Jul 2026 18:17:17 +0530 Subject: [PATCH 11/42] docs(evaluation): add Guardrails concept page --- .../docs/evaluation/concepts/guardrails.mdx | 72 +++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 src/pages/docs/evaluation/concepts/guardrails.mdx diff --git a/src/pages/docs/evaluation/concepts/guardrails.mdx b/src/pages/docs/evaluation/concepts/guardrails.mdx new file mode 100644 index 00000000..cfd19e89 --- /dev/null +++ b/src/pages/docs/evaluation/concepts/guardrails.mdx @@ -0,0 +1,72 @@ +--- +title: "Guardrails" +description: "Evaluate and enforce safety at runtime on live traffic, not after the fact" +--- + +## About + +A **guardrail** is an [evaluation](/docs/evaluation) applied inline to live traffic, where an input or output is checked against a rule and an action is taken based on the result, such as blocking or flagging. Unlike batch evals that score responses after they are generated, guardrails run on the request path itself, in the [Protect gateway](/docs/protect), so decisions can be enforced in real time. + +Guardrails reuse the same eval templates as batch evals (Toxicity, Groundedness, PII Detection, and custom rules), but apply them differently: synchronously at the gateway, with a choice of three outcomes instead of a score. This lets you check both inputs and outputs in one place, while keeping the same eval definitions across your system. + +--- + +## How guardrails differ from evals + +Both use the same templates and judge models, but they answer different questions and run at different times. + +| Aspect | Batch eval | Guardrail | +|---|---|---| +| **When** | After the response is generated | Before the request reaches the provider, or before the response is returned | +| **Where** | Dataset, simulation, or CI/CD | Protect gateway (inline request path) | +| **Result** | Score (0–1), category, or pass/fail | Action (block, warn, or log) | +| **Use case** | Measure quality, understand patterns | Enforce policy in production | + +--- + +## Guardrail structure + +A guardrail policy groups one or more checks and controls how they run. + +Each policy sets: + +- **Checks**: the safety rules to run, each with a type (Toxicity, PII, Prompt Injection, custom, and others) and a threshold +- **Mode**: `enforce` lets checks act on the request, `monitor` records violations without blocking +- **Scope**: where the policy applies, `global` (all keys), `project` (all keys in a project), or `key` (a single virtual key) + +Each check runs at a **stage**, `pre` before the provider call or `post` after it, and resolves to an **action** when it triggers. + +--- + +## Actions + +When a guardrail check triggers (the input or output violates the rule), one of three actions happens: + +| Action | Behavior | Use case | +|---|---|---| +| **Block** | Reject the request with 403 Forbidden | Enforce hard boundaries, such as never allowing prompt injection attempts | +| **Warn** | Add a warning header to the response, allow it through | Flag suspicious content without blocking, such as high-toxicity responses | +| **Log** | Record the violation in audit logs, no change to request or response | Passive monitoring for compliance or analysis | + +--- + +## Runtime vs batch + +Guardrails run at the Protect gateway during request and response processing, so they can make real-time decisions. Batch evals run offline on datasets or after responses are collected. + +Running evals at the gateway means: + +- Responses flagged at the input stage never reach the provider +- Outputs flagged at the output stage are blocked before they are returned +- All decisions are audit-logged for compliance +- Latency is predictable and measured in milliseconds + +The trade-off is that guardrails must evaluate quickly. Complex evals that require extensive reasoning may not be practical as guardrails; simpler rule-based checks and fast scanning work best. + +--- + +## Next steps + +- [Eval templates](/docs/evaluation/concepts/eval-templates): The checks a guardrail reuses and how to customize them. +- [Evaluate via Platform and SDK](/docs/evaluation/features/evaluate): Run the same templates as batch evals. +- [Protect](/docs/protect): Configure and enforce guardrail policies at the gateway. From bae6e2bb33df7bee22e12d7eb675d24e57bbabd0 Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Wed, 8 Jul 2026 18:17:21 +0530 Subject: [PATCH 12/42] docs(evaluation): add Feedback concept page --- .../docs/evaluation/concepts/feedback.mdx | 74 +++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 src/pages/docs/evaluation/concepts/feedback.mdx diff --git a/src/pages/docs/evaluation/concepts/feedback.mdx b/src/pages/docs/evaluation/concepts/feedback.mdx new file mode 100644 index 00000000..b508a407 --- /dev/null +++ b/src/pages/docs/evaluation/concepts/feedback.mdx @@ -0,0 +1,74 @@ +--- +title: "Feedback" +description: "Record corrections and human signals on eval results to improve your evaluator over time" +--- + +## About + +**Feedback** is a human or system signal recorded on an [evaluation](/docs/evaluation) result, marking whether you agree with the judgment. When an eval returns a wrong result, you submit feedback with the correct verdict and an optional explanation. That feedback is stored and used to improve future evals: it drives the correction loop so custom evals learn your domain's definition of quality, and it supplies few-shot examples that steer LLM-based evals toward your standards. + +Feedback turns scattered disagreements into systematic improvement, so evaluators converge on what your team means by correct. + +--- + +## How feedback is captured + +Each feedback entry records: + +| Field | What it holds | +|---|---| +| **Value** | Your verdict: `passed` or `failed`, what the eval should have returned | +| **Explanation** | Why you are correcting the eval, optional, up to 5000 characters | +| **Improvement suggestion** | How the eval could be better, optional, up to 5000 characters | +| **Source** | Where the eval ran: `dataset`, `trace`, `experiment`, `eval_playground`, `prompt`, `observe`, or `sdk` | +| **Source ID** | The row, trace, or item the feedback refers to | + +Feedback is stored in the `Feedback` model alongside the [eval template](/docs/evaluation/concepts/eval-templates), the person who submitted it, and the evaluated row, so you can retrieve every correction for a given eval. + +--- + +## The correction loop + +The correction loop is the pattern that turns feedback into a better eval: run an eval, find rows where it disagrees with your judgment, record the corrections, rewrite the eval rules to include those corrections as examples, then re-run and confirm agreement climbs. + +1. **Establish a baseline**: run a built-in eval on a batch of rows and compare its results to your manual verdicts. +2. **Find disagreements**: a disagreement is any row where the eval and your team reach different verdicts. These rows teach the evaluator something new. +3. **Encode corrections**: write a custom eval whose rule prompt states your domain rules and includes a few disagreement rows as few-shot examples for the judge model. +4. **Re-score and measure**: run the new eval on the same batch and measure agreement against your verdicts. +5. **Iterate**: pull a fresh batch, add new examples, and increment the version. Most evals converge in a few rounds. + +See the [eval correction loop cookbook](/docs/cookbook/evaluation/eval-correction-loop) for a worked end-to-end example. + +--- + +## How feedback feeds back into evals + +Feedback improves evals in two ways. + +**Threshold calibration.** For custom evals, corrections are collected in a feedback store. Calibration sweeps threshold values and picks the one that best matches your corrections, tightening the eval's pass and fail boundary. + +**Few-shot injection.** For LLM-based evals, the SDK retrieves similar past corrections and injects them into the judge prompt as examples. Instead of a generic prompt, the judge sees what good and bad look like for your domain before it scores. + +The [SDK feedback reference](/docs/sdk/evals/feedback) covers the collector, calibration, retrieval, and storage options in detail. + +--- + +## Sources + +Feedback can come from any surface where you evaluate, so you can close the loop wherever the eval ran: + +| Source | Comes from | Use case | +|---|---|---| +| **Dataset** | Dataset column results | Labeling rows where bulk evals got it wrong | +| **Trace** | Live traces in Observe | Marking production responses that were misscored | +| **Experiment** | Experiment results | Marking experiment outcomes that did not match expectations | +| **Eval playground** | The eval tester | Quick feedback on an eval without running it on data | +| **SDK** | Programmatic collection | Collecting corrections at scale in scripts or notebooks | + +--- + +## Next steps + +- [Eval templates](/docs/evaluation/concepts/eval-templates): Write the rule prompts and criteria that feedback refines. +- [Eval correction loop](/docs/cookbook/evaluation/eval-correction-loop): A worked example of turning corrections into a better eval. +- [SDK feedback reference](/docs/sdk/evals/feedback): Calibration, retrieval, and storage in the SDK. From 7698429d19f3d05cafb6fa3641fc9105ef04491c Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Wed, 8 Jul 2026 18:17:25 +0530 Subject: [PATCH 13/42] docs(evaluation): add Ground truth concept page --- .../docs/evaluation/concepts/ground-truth.mdx | 81 +++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100644 src/pages/docs/evaluation/concepts/ground-truth.mdx diff --git a/src/pages/docs/evaluation/concepts/ground-truth.mdx b/src/pages/docs/evaluation/concepts/ground-truth.mdx new file mode 100644 index 00000000..981310ec --- /dev/null +++ b/src/pages/docs/evaluation/concepts/ground-truth.mdx @@ -0,0 +1,81 @@ +--- +title: "Ground truth" +description: "Reference data that reference-based evals compare AI outputs against to measure accuracy and correctness" +--- + +## About + +**Ground truth** is a collection of expected or reference values that reference-based evals compare model outputs against. Instead of measuring subjective qualities like tone or safety, a reference-based eval checks whether the generated output matches the known-correct answer. You upload ground truth as a dataset, then attach it to specific [eval templates](/docs/evaluation/concepts/eval-templates) so those templates can compare each model output to its reference at eval time. + +--- + +## When to use ground truth + +Ground truth is the right choice when you have a clear, correct answer to compare against. Common cases: + +- A question answering system where the correct answer is known in advance +- A code generation tool where you know what the correct code should produce +- A data extraction pipeline where the expected fields and values are labeled +- A classification task where the correct label is on file +- Any task where correct means matching a specific reference output + +If you do not have known-correct answers yet, or correctness is subjective like tone or helpfulness, use a [judge model](/docs/evaluation/concepts/judge-models) eval instead. + +--- + +## Evals that use ground truth + +Several built-in templates compare against a reference value: + +- [Ground Truth Match](/docs/evaluation/builtin/ground-truth-match): pass or fail on whether the output matches the reference value semantically +- Answer Similarity: a similarity score between the output and a reference answer +- Fuzzy Match: approximate string matching with configurable tolerance +- Retrieval metrics such as Recall@K and Mean Average Precision: measure whether a retrieved set contains the expected items + +Each template documents the input keys it expects. When you attach ground truth to a template, you map your dataset columns to those keys. + +--- + +## How ground truth data is structured + +Ground truth is stored as a dataset with named columns, where each row is one example. At minimum it needs: + +- **Input columns**: the data the model receives, such as the question column for a question-answering eval +- **Expected output column**: the correct answer the eval compares against. Every row must have one +- **Optional explanation column**: any extra context the eval can use, such as the reasoning behind the answer + +The platform stores the dataset as JSON rows with named columns. + +--- + +## Mapping ground truth to eval inputs + +When you attach ground truth to an eval template, you configure two mappings: + +**Variable mapping** connects each eval template input to a ground truth column. For example, if the template needs a `question` input, you map it to the `prompt` column. At eval time the platform uses this mapping to pull the right column value for each row. + +**Role mapping** designates which column is the expected output, and optionally which column carries an explanation. It tells the eval which ground truth value to compare against. + +The platform validates these mappings when you save them, so every mapped column must exist in your dataset. + +--- + +## Retrieval and few-shot injection + +Ground truth can also calibrate the judge by injecting similar examples into the eval prompt, called **few-shot grounding**. When enabled, the platform: + +1. Embeds your ground truth dataset so similar examples can be found +2. At eval runtime, finds the ground truth rows most similar to the current input +3. Includes those rows in the judge prompt as reference calibration + +This helps the judge apply your correctness criteria without you rewriting the prompt. + +Retrieval requires the embedding status to be `Completed`. The platform embeds ground truth asynchronously after you save a mapping, and once embedding finishes, retrieval starts on the next eval run. If you change the variable mapping, the embeddings are marked stale and re-embedded automatically. + +--- + +## Next steps + +- [Ground Truth Match](/docs/evaluation/builtin/ground-truth-match): The built-in eval that compares output to a reference value. +- [Evaluate via Platform and SDK](/docs/evaluation/features/evaluate): Attach ground truth and run evals against it. +- [Judge models](/docs/evaluation/concepts/judge-models): Use a judge when there is no fixed correct answer. From cf6b23dff0c15d5e7622ba997a7fbd44f544f6f1 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Wed, 8 Jul 2026 19:50:11 +0530 Subject: [PATCH 14/42] docs(evaluation): address review on eval-types and add the type-choice mental model --- .../docs/evaluation/concepts/eval-types.mdx | 49 ++++++++++++------- 1 file changed, 31 insertions(+), 18 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/eval-types.mdx b/src/pages/docs/evaluation/concepts/eval-types.mdx index 419fcaba..62677e6b 100644 --- a/src/pages/docs/evaluation/concepts/eval-types.mdx +++ b/src/pages/docs/evaluation/concepts/eval-types.mdx @@ -1,39 +1,52 @@ --- title: "Eval types" -description: "The three eval types: Agent Evaluator, LLM-as-Judge, and Code Eval, and when to reach for each." +description: "The three eval types: Agent Evaluator, LLM-as-Judge, and Code Eval, and when to reach for each" --- -Every eval is authored as one of three types. The type decides how the eval reaches its verdict, what it can reason over, and whether a [judge model](/docs/evaluation/concepts/judge-models) is involved. It is the first choice you make when you create an eval. +## Three ways to reach a verdict -## Agent Evaluator +Every eval is authored as one of three types. The type decides how the eval reaches its verdict, what it can reason over, and whether a [judge model](/docs/evaluation/concepts/judge-models) is involved. It's the first choice you make when you create an eval. + +### Agent Evaluator A reasoning evaluator that runs multiple turns and can use tools and knowledge bases to reach a judgment. Reach for it when a single prompt cannot capture the check, like scoring a multi-step agent transcript or a judgment that needs to look something up. It is the most capable type, and the newest. -Requires a judge model. +### LLM-as-Judge + +A judge model reads the response, applies the [template](/docs/evaluation/concepts/eval-templates)'s criteria in a single pass, and returns a result plus a reason. It is the workhorse for subjective, context-dependent quality: safety, tone, faithfulness, instruction adherence, and any custom rule you can write in plain language. -## LLM-as-Judge +### Code Eval -A judge model reads the response, applies the template's criteria in a single pass, and returns a result plus a reason. It is the workhorse for subjective, context-dependent quality: safety, tone, faithfulness, instruction adherence, and any custom rule you can write in plain language. +Deterministic logic runs in a sandbox (Python or JavaScript) and computes the result directly from the text. Given the same input it always returns the same output and calls no model. This is the type behind format validation (valid JSON, email, URL), overlap and similarity metrics (BLEU, ROUGE, edit distance, embedding similarity), and retrieval metrics (recall@k, precision@k, NDCG, MRR). -Requires a judge model. +## One check, three ways -## Code Eval +Take one check: is this answer grounded in the source document? -Deterministic logic runs in a sandbox (Python or JavaScript) and computes the result directly from the text. Given the same input it always returns the same output, calls no model, and needs no API key. This is the type behind format validation (valid JSON, email, URL), overlap and similarity metrics (BLEU, ROUGE, edit distance, embedding similarity), and retrieval metrics (recall@k, precision@k, NDCG, MRR). +- A **Code Eval** can only measure overlap: an embedding-similarity score between the answer and the source. Fast and repeatable, but a paraphrased hallucination can slip through +- An **LLM-as-Judge** reads both and judges faithfulness in one pass, returning a verdict and the reason a claim isn't supported +- An **Agent Evaluator** goes further: it can search the knowledge base behind the answer and verify it claim by claim -No judge model, and it runs locally. +Same check, three depths. The deeper you go, the more the verdict costs in time and determinism. ## Which type to reach for -- **Code Eval** when the check is objective and computable -- **LLM-as-Judge** when it needs an understanding of meaning -- **Agent Evaluator** when one pass is not enough +The three types are a ladder: a Code Eval computes the verdict, an LLM-as-Judge reads and judges it, an Agent Evaluator investigates before judging. Each step up buys reasoning power and costs speed and determinism, so climb only as high as the check requires. + +|"Yes"| CE["Code Eval"] + Q1 -->|"No"| Q2["One read of the response enough?"] + Q2 -->|"Yes"| LJ["LLM-as-Judge"] + Q2 -->|"No"| AE["Agent Evaluator"] +`} /> -| Type | Judge model | Returns a reason | Runs without an API key | -|---|---|---|---| -| Agent Evaluator | Yes | Yes | No | -| LLM-as-Judge | Yes | Yes | No | -| Code Eval | No | No | Yes | +| Type | Judge model | Returns a reason | +|---|---|---| +| Agent Evaluator | Yes | Yes | +| LLM-as-Judge | Yes | Yes | +| Code Eval | No | No | ## Keep exploring From 729b88d0b4f5e851a79ac90e8318473be79dea0c Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Thu, 9 Jul 2026 12:52:30 +0530 Subject: [PATCH 15/42] docs(evaluation): address review on composite-evals --- .../evaluation/concepts/composite-evals.mdx | 98 ++++++++++++++----- 1 file changed, 75 insertions(+), 23 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/composite-evals.mdx b/src/pages/docs/evaluation/concepts/composite-evals.mdx index 1f05a016..43fe4d96 100644 --- a/src/pages/docs/evaluation/concepts/composite-evals.mdx +++ b/src/pages/docs/evaluation/concepts/composite-evals.mdx @@ -1,15 +1,13 @@ --- -title: "Composite evals & groups" -description: "How a composite eval rolls several child templates into one score, and how eval groups apply a set of templates together." +title: "Composite evals" +description: "Roll several eval child templates into one score" --- -A **composite eval** rolls several child templates into a single score, so one check can stand for several at once. An **eval group** is a named set of templates you apply together, so a whole quality bar travels as one unit. Both let you treat "good" as more than one measurement without wiring the same pieces together by hand every time. +## What are they -## Composite evals +A **composite eval** rolls several child [templates](/docs/evaluation/concepts/eval-templates) into a single score, so one check can stand for several at once. Every template is either `single` or `composite`. A composite template holds an ordered list of child templates. When it runs, the parent scores each child on the same input, then combines the child scores into one parent score with an [aggregation function](#aggregation-functions). -Every template is either `single` or `composite`. A composite template holds an ordered list of **child** templates. When it runs, the parent scores each child on the same input, then combines the child scores into one parent score with an [aggregation function](#aggregation-functions). - -Children share one output type, either pass/fail, score, or choices, so their scores are comparable before they are combined. A composite made of graded children produces a graded score; one made of pass/fail children produces a pass/fail-style score. +Children share one [output type](/docs/evaluation/concepts/output-types), either pass/fail, score, or choices, so their scores are comparable before they are combined. A composite made of graded children produces a graded score; one made of pass/fail children produces a pass/fail-style score. Reach for a composite when "good" is several checks at once, for example faithful *and* on-tone *and* complete. Instead of reading three separate results, you get one score that already reflects all three, and one threshold to gate on. @@ -17,15 +15,75 @@ Reach for a composite when "good" is several checks at once, for example faithfu The parent combines child scores with one of five functions. Every child score is normalized to a 0 to 1 range first, so they can be compared on the same scale. -| Function | What it returns | -|---|---| -| Weighted average | Each child score times its weight, divided by the total weight. This is the default | -| Average | The plain mean of the child scores | -| Minimum | The lowest child score, a strict gate where every child has to do well | -| Maximum | The highest child score | -| Pass rate | The fraction of children that individually meet their own pass threshold | +The examples below all score the same response with three graded children: Groundedness 0.9 with weight 2.0, Tone 0.7 and Completeness 0.8 with weight 1.0. + +### Weighted average + +Each child score times its weight, divided by the total weight. This is the default. Use it when some children matter more than others. + +weight 2.0"] --> AGG["Weighted average"] + C2["Tone 0.7
weight 1.0"] --> AGG + C3["Completeness 0.8
weight 1.0"] --> AGG + AGG --> P["Composite score 0.825"] +`} /> + +On the example: (0.9 × 2.0 + 0.7 + 0.8) / 4.0 = 0.825, and one threshold on that single number gates all three checks at once. + +### Average + +The plain mean of the child scores; weights are ignored. On the example: (0.9 + 0.7 + 0.8) / 3 = 0.8. + + AGG["Average"] + C2["Tone 0.7"] --> AGG + C3["Completeness 0.8"] --> AGG + AGG --> P["Composite score 0.8"] +`} /> + +### Minimum + +The lowest child score, a strict gate where every child has to do well. Use it when any one failure should sink the whole score. On the example: 0.7, the Tone score. + + AGG["Minimum"] + C2["Tone 0.7"] --> AGG + C3["Completeness 0.8"] --> AGG + AGG --> P["Composite score 0.7"] +`} /> + +### Maximum + +The highest child score, when clearing the bar on any one check is enough. On the example: 0.9, the Groundedness score. + + AGG["Maximum"] + C2["Tone 0.7"] --> AGG + C3["Completeness 0.8"] --> AGG + AGG --> P["Composite score 0.9"] +`} /> + +### Pass rate + +The fraction of children that individually meet their own pass threshold. Use it when you care how many checks passed rather than by how much. On the example, with each child's threshold at 0.8: two of the three pass, so 0.67. + + AGG["Pass rate"] + C2["Tone 0.7 fails"] --> AGG + C3["Completeness 0.8 passes"] --> AGG + AGG --> P["Composite score 0.67"] +`} /> -Pick the function to the shape of the check. Use minimum when any one failure should sink the whole score, pass rate when you care how many checks passed rather than by how much, and weighted average when some children matter more than others. ## Weights and pinned versions @@ -36,17 +94,11 @@ Two settings on each child keep a composite stable as it grows. Weights only change the weighted average function; the other four ignore them. Pinning matters most for a composite you rely on in CI, where a silent change to a child would quietly move the gate. -## Eval groups - -An **eval group** is a named collection of templates you apply together in a single run. Where a composite folds its children into one combined score, a group runs its templates side by side and reports each result on its own. - -Use a group when a whole suite belongs together, for example a safety bar, a tone bar, and a retrieval bar. You attach the group by its name instead of hand-picking the same templates every time, so the bar stays consistent across datasets, simulations, and traces. - ## Keep exploring - - Run a composite or a group on any surface + + Run a composite on any surface Write a child template of your own From fc2744f076e1a8b497fe603a3ef2eba537dc701d Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Thu, 9 Jul 2026 13:03:20 +0530 Subject: [PATCH 16/42] docs(evaluation): fix Dive deeper card, futureagi-models page is gone Repoints the judge-model card to concepts/judge-models, where that content now lives after a84dce32 folded it in. --- src/pages/docs/evaluation/features/evaluate.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/docs/evaluation/features/evaluate.mdx b/src/pages/docs/evaluation/features/evaluate.mdx index 3348afca..c20c55b4 100644 --- a/src/pages/docs/evaluation/features/evaluate.mdx +++ b/src/pages/docs/evaluation/features/evaluate.mdx @@ -29,7 +29,7 @@ The online how-to for scoring live traces lives in Observe; this hub only points 156 templates across 14 groups - + Pick a judge model for your evals From c3fca89148d0dc25ef5961f6f291bba78f086995 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Thu, 9 Jul 2026 13:06:38 +0530 Subject: [PATCH 17/42] docs(evaluation): address review and expand the Overview --- src/pages/docs/evaluation/index.mdx | 51 ++++++++++++++++++++++++----- 1 file changed, 43 insertions(+), 8 deletions(-) diff --git a/src/pages/docs/evaluation/index.mdx b/src/pages/docs/evaluation/index.mdx index 8a244893..8a8b3edb 100644 --- a/src/pages/docs/evaluation/index.mdx +++ b/src/pages/docs/evaluation/index.mdx @@ -1,18 +1,46 @@ --- title: "Overview" -description: "Evaluations measure how well your AI achieves the user's goal, scored on metrics like accuracy, safety, and tone." +description: "Measure how well your AI achieves the user's goal" --- -Evaluations measure how well your AI achieves the user's goal, scored on a metric like task completion, factual accuracy, safety, or tone. You define what "good" means once, and Future AGI scores every response against it, returning a result and a plain-language reason for each one. +Evaluation is Future AGI's quality measurement layer: it scores every response your AI produces against a definition of "good" that you control. -Evaluation is the middle of a loop, not a standalone tool: you observe what your agent did, evaluate its quality, optimize the prompt or model, and enforce a bar before anything ships. The same eval runs everywhere your work lives, so a score means the same thing on a dataset, a live trace, or a pull request. +## What is evaluation? -Four objects carry the whole product: a [template](/docs/evaluation/concepts/eval-templates) defines what to measure, a config points it at your data, a run executes, and a [score](/docs/evaluation/concepts/eval-results) comes out. Future AGI ships 156 built-in evals, or you write your own. +An LLM's output is free text, and whether it is *right* is a judgment. Evaluation turns that judgment into a measurement. You define what "good" means once, as an eval, and the platform applies it to every response automatically, the same way every time. + +Each eval scores one goal on one metric, for example: + +- **Task completion**: did the response do what was asked +- **Factual accuracy**: are its claims true to the source +- **Safety**: is it free of toxicity, prompt injection, and data leaks +- **Tone**: does it speak the way your product should + +Every run returns a score (pass/fail, a number, or a category) and, for judged evals, a plain-language reason. Because the definition is fixed, the same bar is applied to every response: scores stay comparable across a dataset, a live trace, or a pull request, and a threshold on that score becomes a decision you can automate instead of a vibe check. + +## The quality loop + +Evaluation is the middle of a loop, not a standalone tool: + +what the agent did"] --> E["Evaluate
score it against your bar"] + E --> OPT["Optimize
the prompt or model"] + OPT --> G["Enforce
gate the release"] + G --> O + style E fill:#2f2f2f,stroke:#ffffff,stroke-width:2px +`} /> + +- [Observe](/docs/observe) records what your agent did, and its traces become eval inputs +- Evaluate scores each response and shows the result back on the trace +- [Optimization](/docs/optimization) consumes the scores to improve the prompt or model +- A threshold turns the score into a merge gate in [CI/CD](/docs/evaluation/features/cicd) before anything ships ## Start here - + Every surface you can run an eval on, and how to get going @@ -22,7 +50,14 @@ Four objects carry the whole product: a [template](/docs/evaluation/concepts/eva ## Understand the model -A few short pages give you the whole mental model behind Evaluations: +Four objects carry the whole product: + +- A **[template](/docs/evaluation/concepts/eval-templates)** defines what to measure +- A **config** points it at your data +- A **run** executes +- A **[score](/docs/evaluation/concepts/output-types)** comes out + +A few short pages give you the whole mental model: @@ -34,14 +69,14 @@ A few short pages give you the whole mental model behind Evaluations: What decides the score, and how to choose one - + The value, optional reason, and aggregates you get back ## How it connects -Evaluation sits in the middle of Future AGI and feeds on the rest: [Observe](/docs/observe) turns live traces into eval inputs and shows scores back on the trace, datasets and simulations are where offline evals run, optimization consumes scores to improve a prompt, and thresholds become production gates. +Beyond the loop, the same evals run offline: [datasets](/docs/dataset) score rows in bulk, and [simulations](/docs/simulation) score synthetic conversations before anything reaches production. From 8ce4882b82c907bdf314b7c6fb2748f5fbdd09e6 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Thu, 9 Jul 2026 13:25:10 +0530 Subject: [PATCH 18/42] docs(evaluation): address review on understanding-evaluation --- .../concepts/understanding-evaluation.mdx | 22 ++++++++++++------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx b/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx index 1b416179..03aefe35 100644 --- a/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx +++ b/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx @@ -1,24 +1,30 @@ --- title: "Understanding Evaluation" -description: "The four objects behind every eval: template, config, run, and score, and the quality loop they sit in." +description: "The four objects behind every eval, and the loop they sit in" --- -Evaluation is Future AGI's quality measurement layer. You define what "good" means once, and the platform scores every response against that definition, returning a result and a plain-language reason for each one. Because the same definition runs everywhere your work lives, a score means the same thing on a dataset, a simulation, a live trace, or a pull request. - ## The four objects -Four objects carry the whole product. Learn these and the rest is detail: +You define what "good" means once, and the platform scores every response against that definition. Four objects carry the whole product. Learn these and the rest is detail: -- A **template** defines *what* to measure: the criteria, the expected output type, and a pass threshold. Templates are reusable and versioned, and are either built by Future AGI or written by you +- A **[template](/docs/evaluation/concepts/eval-templates)** defines *what* to measure: the criteria, the expected output type, and a pass threshold. Templates are reusable and versioned, and are either built by Future AGI or written by you - A **config** defines *how* to measure for one run: which [judge model](/docs/evaluation/concepts/judge-models), how your data maps to the template's inputs, and the run settings - A **run** is one execution: a template plus a config plus one unit of data, from a single row to millions - A **score** is the outcome: a value (pass or fail, a number, or a category), an optional reason, and the metadata to trace it back -Read it left to right: a template is the definition, a config points it at data, a run executes, and a score comes out. Everything else in Evaluations elaborates on one of these four. +what to measure"] --> C["Config
pointed at your data"] + C --> R["Run
one execution"] + R --> S["Score
the outcome"] +`} /> + +Read it left to right: a template is the definition, a config points it at data, a run executes, and a score comes out. Everything else in Evaluations elaborates on one of these four. And because the same definition runs everywhere your work lives, a score means the same thing on a dataset, a simulation, a live trace, or a pull request. ## The quality loop -Evaluation is the middle of a loop, not a standalone tool. You [observe](/docs/observe) what your agent did, evaluate its quality, optimize the prompt or model, and enforce a bar before anything ships. Traces become eval inputs, scores drive optimization, and thresholds become production gates. +Evaluation is the middle of a loop, not a standalone tool. You [observe](/docs/observe) what your agent did, evaluate its quality, [optimize](/docs/optimization) the prompt or model, and enforce a bar before anything ships. Traces become eval inputs, scores drive optimization, and thresholds become production gates. ## How an eval reaches a verdict @@ -42,7 +48,7 @@ The type decides how the verdict is reached and whether a judge model is involve The models that read and score a response - + The value, optional reason, and aggregates From d30c17ad4bc4ac07b337843f8d6706f18c803099 Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 14:31:57 +0530 Subject: [PATCH 19/42] docs(evaluation): rewrite Eval templates & versions concept What a template holds (criteria, required inputs, output type, threshold, optional reason), input mapping with {{variables}}, and the versions model (numbered snapshots, default, pin so a re-run scores the same and a CI gate stays stable). Rewritten for one-read clarity: no '## About', minimal frontmatter, forward cards, no em-dashes, all links resolve. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../evaluation/concepts/eval-templates.mdx | 90 ++++++++----------- 1 file changed, 36 insertions(+), 54 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index 5e36c291..913d35db 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -1,71 +1,53 @@ --- -title: "Eval Templates: Built-in and Custom Evaluation Templates" -description: "Explains what evaluation templates are, the difference between built-in and custom templates, and how output types determine what an eval returns." +title: "Eval templates & versions" +description: "A template is the reusable definition of what an eval checks. Each version saves a snapshot of it, so an eval keeps giving the same result even after you edit the template." --- -## About +A template is where you define what an eval checks. Whether you check for toxicity, verify a response stays grounded in its source, or enforce a company rule, that logic lives in the template and nowhere else. You write it once and reuse it by name across a dataset, a simulation, a live trace, or a check that runs on every pull request. A template is either built by Future AGI (built-in) or written by you (custom). -An eval template is the definition of what to measure. It contains the criteria the judge model will apply to each response and specifies what kind of result to return. You create a template once and reuse it across any dataset, simulation, experiment, or SDK call. +## What a template holds -Templates are the reusable unit of evaluation logic. Whether you're checking for toxicity, verifying that a response stays grounded in a source document, or enforcing a company-specific rule, the logic lives in the template. +- **Criteria** the judge applies, written as a rule the model can follow +- **Required inputs**: the keys the template needs, like `output` (the response) and `context` (the source it should stay grounded in) +- **Output type**: whether the result is pass or fail, a score, or a category, covered in [Output types](/docs/evaluation/concepts/output-types) +- **Pass threshold**: for a score or a category, the line that turns the raw value into a pass or fail +- **Reason** (optional): a plain-language explanation of the verdict, when the eval produces one ---- - -## Built-in vs custom templates - -| | Built-in | Custom | -|---|---|---| -| **Who writes the criteria** | Future AGI | You | -| **How to access** | Select from the template list in the UI or pass the name to the SDK | Create via UI or API, then use by name | -| **Covers** | 70+ categories: quality, safety, factuality, RAG, bias, format, audio, image | Any domain-specific, business, or regulatory rule you define | -| **Required inputs** | Defined per template (e.g. `input`, `output`, `context`) | You define the required keys in the template config | - -See [Built-in evals](/docs/evaluation/builtin) for the full list of available templates. - -See [Create custom evals](/docs/evaluation/features/custom) for how to write your own. - ---- +## Required inputs and mapping -## Output types +A template declares the input keys it needs, and at run time you map your real data to those keys. A groundedness template, for example, needs `output` and `context`; you point each one at the right column or field. -Every template returns one of three output types: +Custom templates define their own keys with `{{variable}}` placeholders in the rule prompt. The names you write become the inputs you must supply: -| Type | Description | Example | -|---|---|---| -| **Pass/Fail** | Binary result: 1.0 for pass, 0.0 for fail | Toxicity check: passed or failed | -| **Score (percentage)** | Numeric value between 0 and 100 | Groundedness: 87 out of 100 | -| **Deterministic choices** | Categorical result from a defined set of options | Tone classification: `formal`, `informal`, `neutral` | - -Every result also includes a **reason**: a plain-language explanation of why the judge assigned that result. This makes it possible to understand failures without reviewing each response manually. - ---- - -## Required keys and input mapping +```text +Rate whether {{output}} is fully supported by {{context}}. +``` -Templates declare the input keys they expect. For example, a groundedness template might require `output` (the model response) and `context` (the source document). When you run an eval, you map your actual data to these keys. +Here `output` and `context` become the required inputs for that template. -**In the UI**: When you add a template to a dataset or simulation, the platform shows a mapping form. You select which column corresponds to each required key. +## Versions -**In the SDK**: Pass a dict where the keys match what the template expects: +Every time you change a template, Future AGI saves the previous one as a numbered version, so nothing you already ran gets overwritten. One version is the **default**: the one a new run uses. -```python -result = evaluator.evaluate( - template=Groundedness(), - input={ - "output": "The Eiffel Tower is in Paris.", - "context": "The Eiffel Tower is a wrought-iron lattice tower in Paris, France.", - }, -) -``` +Each version is a frozen copy of the exact criteria and threshold. Because of that, you can **pin** a run to a specific version: re-run the eval a month later and it scores the same way, and an eval that gates your pull requests keeps behaving the same even after you edit the template. -Built-in templates have fixed required keys documented in the template reference. Custom templates let you define any keys using `{{variable_name}}` placeholders in the rule prompt: the key names you use in the prompt become the required keys you must supply at run time. +## Single or composite ---- +A template is **single** (it runs on its own) or **composite** (it aggregates several child templates into one score). Reach for a composite when "good" means several checks at once, like faithful and on-tone and complete. -## Next steps +## Keep exploring -- [Built-in evals](/docs/evaluation/builtin): Full list of available templates with required keys and output types. -- [Create custom evals](/docs/evaluation/features/custom): Write your own criteria and rule prompts. -- [Eval types](/docs/evaluation/concepts/eval-types): LLM as Judge, Deterministic, Statistical Metric, and LLM as Ranker. -- [Judge models](/docs/evaluation/concepts/judge-models): How the model applies a template to produce a result. -- [Output types](/docs/evaluation/concepts/output-types): What the output of an eval run looks like + + + Pass or fail, score, and category, and how a threshold decides + + + The grounding you pass as the context input + + + The model that applies a template's criteria + + + 156 ready templates, each with its inputs and output type + + From 66becd876edb7c814888d80b983e4315bfd81e5d Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 14:49:54 +0530 Subject: [PATCH 20/42] docs(evaluation): drop Keep-exploring card that duplicated the Next nav The 'Judge models' card repeated the auto-generated Next link; remove it and switch the group to cols=3 (3 cards). Co-Authored-By: Claude Opus 4.8 (1M context) --- src/pages/docs/evaluation/concepts/eval-templates.mdx | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index 913d35db..cba2d584 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -37,16 +37,13 @@ A template is **single** (it runs on its own) or **composite** (it aggregates se ## Keep exploring - + Pass or fail, score, and category, and how a threshold decides The grounding you pass as the context input - - The model that applies a template's criteria - 156 ready templates, each with its inputs and output type From fa428d9f1ad163b6a042cec59dc34ac4eb5fc642 Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 14:53:27 +0530 Subject: [PATCH 21/42] docs(evaluation): rewrite Judge models concept What a judge is (reads and scores, never edits the output), the Turing/Protect model table on this page, bring-your-own judge, modality (image/audio need a multimodal judge), and how to choose. Fixed the audio-modality error (turing_small is text+image only). Keep-exploring cards avoid the prev/next neighbors. No About, minimal frontmatter, no em-dashes, all links resolve. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../docs/evaluation/concepts/judge-models.mdx | 83 ++++++++----------- 1 file changed, 35 insertions(+), 48 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/judge-models.mdx b/src/pages/docs/evaluation/concepts/judge-models.mdx index c42940c3..b69ebffb 100644 --- a/src/pages/docs/evaluation/concepts/judge-models.mdx +++ b/src/pages/docs/evaluation/concepts/judge-models.mdx @@ -1,64 +1,51 @@ --- -title: "Judge Models: Choosing the Right Evaluation Scorer" -description: "Explains what a judge model is, how it scores AI responses, and how to choose the right judge model for your specific evaluation use case." +title: "Judge models" +description: "A judge model reads a response and applies a template's criteria to produce a result and a reason. Future AGI ships judges built for evaluation, and you can bring your own." --- -## About +A judge model is the model that reads a response and applies an eval template's criteria to produce a result and a reason. It receives the text to evaluate, the template's rule, and the required inputs, then returns a verdict. A judge only reads and scores; it never generates or edits your AI's output. Agent Evaluator and LLM-as-Judge evals use a judge; Code Evals do not. -A judge model is the model that reads each response and applies the eval template criteria to produce a result. When you run an evaluation, the judge receives the text to evaluate, the template's rule prompt, and the required inputs, then returns a result and a reason. +## Future AGI judge models -The judge model determines how accurately and how quickly each response gets scored. Choosing the right one lets you balance precision and performance for your specific workload. - ---- - -## How a judge scores a response - -1. The platform constructs a prompt from the eval template criteria and the row's input values. -2. The judge model receives this prompt and reads the response being evaluated. -3. The judge returns a result (pass/fail, score, or category) and a reason explaining the judgment. -4. The platform stores the result and reason for that row. - -The judge model does not generate or modify your AI's responses. It only reads and scores them. - ---- - -## Available judge models - -Future AGI provides a set of proprietary models built specifically for evaluation, not general-purpose chat or generation: - -- **TURING_LARGE** `turing_large`: Flagship evaluation model that delivers best-in-class accuracy across multimodal inputs (text, images, audio). Recommended when maximal precision outweighs latency constraints -- **TURING_SMALL** `turing_small`: Compact variant that preserves high evaluation fidelity while lowering computational cost. Supports text and image evaluations -- **TURING_FLASH** `turing_flash`: Latency-optimised version of TURING, providing high-accuracy assessments for text and image inputs with fast response times -- **PROTECT** `protect`: Real-time guardrailing model for safety, policy compliance, and content-risk detection. Offers very low latency on text and audio streams and permits user-defined rule sets -- **PROTECT_FLASH** `protect_flash`: Ultra-fast binary guardrail for text content. Designed for first-pass filtering where millisecond-level turnaround is critical +Future AGI ships proprietary models built for evaluation, not for general-purpose chat: | Model | Code | Inputs | Best for | Latency | -| --- | --- | --- | --- | --- | +|---|---|---|---|---| | TURING_LARGE | `turing_large` | Text, image, audio | Max accuracy, multimodal evals | Higher | -| TURING_SMALL | `turing_small` | Text, image | High fidelity, lower cost | Medium | +| TURING_SMALL | `turing_small` | Text, image | High fidelity at lower cost | Medium | | TURING_FLASH | `turing_flash` | Text, image | Fast, high-accuracy evals | Low | -| PROTECT | `protect` | Text, audio | Safety, guardrails, user-defined rules | Low | +| PROTECT | `protect` | Text, audio | Safety, guardrails, user rules | Low | | PROTECT_FLASH | `protect_flash` | Text | First-pass binary filtering | Ultra-low | -You can also bring your own model using the [custom models](/docs/evaluation/features/custom-models) integration. This is useful when you need a domain-specific fine-tuned model, want to keep inference in a specific cloud region, or already pay for a model you want to use as the judge. - ---- +## Bring your own judge -## How to choose a judge +You can also use your own model as the judge, from OpenAI, Bedrock, SageMaker, Vertex AI, Azure, or a custom endpoint. Reach for a custom judge when you need a domain-tuned model, must keep inference in a region, or already pay for a model you want to reuse. See [Use custom models](/docs/evaluation/features/custom-models). -| Situation | Recommended model | -|---|---| -| Maximum accuracy matters more than speed | `turing_large` | -| High quality at reasonable cost | `turing_small` | -| Large-scale runs where speed is important | `turing_flash` | -| Safety and guardrail checks | `protect` or `protect_flash` | -| Evaluating images or audio | `turing_large` or `turing_small` | -| Domain-specific or compliance requirements | [Custom model](/docs/evaluation/features/custom-models) | +## Modality ---- +A judge can only score inputs it can read. Image evals need `turing_large` or `turing_small`; audio evals need `turing_large` or `protect`. A text-only judge cannot score an image or an audio input, so pick a multimodal judge when the eval carries one. -## Next steps +## How to choose -- [Use custom models](/docs/evaluation/features/custom-models): Bring your own model as the judge. -- [Eval templates](/docs/evaluation/concepts/eval-templates): The criteria the judge applies. -- [Output types](/docs/evaluation/concepts/output-types): What the judge produces after scoring +| Situation | Judge | +|---|---| +| Maximum accuracy over speed | `turing_large` | +| High quality at a reasonable cost | `turing_small` | +| Large runs where speed matters | `turing_flash` | +| Safety and guardrail checks | `protect` or `protect_flash` | +| Image or audio inputs | a multimodal judge (see Modality) | +| Domain-specific or compliance need | a custom judge | + +## Keep exploring + + + + Which types use a judge, and which run without one + + + Bring your own model as the judge + + + What the judge produces after scoring + + From 47d6bc6a62bf50e4653e9826951989e0360f809c Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 15:21:11 +0530 Subject: [PATCH 22/42] docs(evaluation): add Context concept What context is (the grounding an eval checks against), the RAG/retrieval evals that read it, knowledge base vs context, agent multi-hop context, and how to provide it. Traced runs still map the span attribute to the eval input (not auto-mapped). No About, minimal frontmatter, no em-dashes, cols=3, all links resolve. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../docs/evaluation/concepts/context.mdx | 46 +++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 src/pages/docs/evaluation/concepts/context.mdx diff --git a/src/pages/docs/evaluation/concepts/context.mdx b/src/pages/docs/evaluation/concepts/context.mdx new file mode 100644 index 00000000..ab9349d7 --- /dev/null +++ b/src/pages/docs/evaluation/concepts/context.mdx @@ -0,0 +1,46 @@ +--- +title: "Context" +description: "Context is the grounding you give an eval: the facts or retrieved documents a response should stay true to. It is how groundedness and RAG evals know what correct means." +--- + +Context is the grounding you give an eval. It is the facts, retrieved documents, tool outputs, or conversation history that a response should stay true to. Without context, an eval can judge whether an answer reads well; with context, it can judge whether the answer is actually supported by the source it was meant to use. + +You pass context as an input to the eval, the same way you pass the response. It is a `string` or a `list[string]`: one document, or the set of chunks your retrieval returned for that query. + +## Evals that use context + +Context is what the RAG and retrieval family of evals reads: + +- **Groundedness** checks that the response only claims things the context supports +- **Context adherence** checks that the response follows what the context says +- **Context relevance** checks that the retrieved context actually addresses the query +- **Chunk attribution** traces each claim back to the chunk it came from +- **Chunk utilization** checks whether the retrieval returned the chunks the answer needed + +An eval that needs context declares it as a required input. If you do not map it, the eval has nothing to ground against. + +## Knowledge base and context + +A **knowledge base** is the full set of sources your app can retrieve from, like all of your product or support documentation. **Context** is the slice of it that retrieval pulls for a single query. An eval scores against the context that was actually used for that response, not the whole knowledge base, so it measures the retrieval you really ran. + +## Context in an agent + +An agent often retrieves more than once, refining its query as it goes. Each retrieval hop carries its own context, and the final answer draws on the accumulated set. On a traced agent run, each retrieval hop's context lives on its own retriever span, so once the eval is pointed at the attribute that holds it, both each hop's retrieval quality and the sufficiency of the final context can be scored hop by hop. + +## Providing context + +You give an eval its context wherever the eval runs. In a dataset, map the column that holds your context to the template's `context` input. In the SDK, pass it as the `context` input alongside the response. On a traced run, the context arrives in the trace as a span attribute, and you map that attribute to the template's `context` input yourself, since its name depends on how your app is instrumented. + +## Keep exploring + + + + How a template declares the inputs it needs + + + The RAG and retrieval evals that read context + + + How context-dependent judgments get made + + From 983075ee0d34cb0f142254148f64475f67415236 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Thu, 9 Jul 2026 15:51:57 +0530 Subject: [PATCH 23/42] docs(evaluation): align Create a custom eval, move how-to pages under guides/ Review fixes on the custom eval page: the Model step lists all three options from the product UI (Use Future AGI Agents / Use other LLMs / Function based), output types and optional settings set as bullets, SDK response comment shows the real envelope, footer cards flow forward. Move the four feature pages to the sidebar's guides/ structure (running-evaluations, custom-evals, custom-models, cicd), sync titles to the sidebar labels, and repoint every reference directly with no new redirect entries. The Overview hero now calls out audio-native scoring. Signed-off-by: khushalsonawat --- src/lib/navigation.ts | 8 ++-- src/lib/redirects.ts | 10 ++--- .../docs/cookbook/decrease-hallucination.mdx | 2 +- .../evaluation/eval-correction-loop.mdx | 2 +- .../docs/dataset/features/experiments.mdx | 4 +- .../docs/dataset/features/run-prompt.mdx | 8 ++-- .../evaluation/concepts/composite-evals.mdx | 4 +- .../evaluation/concepts/eval-templates.mdx | 4 +- .../docs/evaluation/concepts/eval-types.mdx | 2 +- .../docs/evaluation/concepts/judge-models.mdx | 6 +-- .../docs/evaluation/concepts/output-types.mdx | 4 +- .../evaluation/{features => guides}/cicd.mdx | 8 ++-- .../custom.mdx => guides/custom-evals.mdx} | 42 ++++++++++++------- .../{features => guides}/custom-models.mdx | 10 ++--- .../running-evaluations.mdx} | 10 ++--- src/pages/docs/evaluation/index.mdx | 8 ++-- src/pages/docs/faq.mdx | 2 +- .../simulation/features/prompt-simulation.mdx | 2 +- 18 files changed, 73 insertions(+), 63 deletions(-) rename src/pages/docs/evaluation/{features => guides}/cicd.mdx (98%) rename src/pages/docs/evaluation/{features/custom.mdx => guides/custom-evals.mdx} (73%) rename src/pages/docs/evaluation/{features => guides}/custom-models.mdx (97%) rename src/pages/docs/evaluation/{features/evaluate.mdx => guides/running-evaluations.mdx} (97%) diff --git a/src/lib/navigation.ts b/src/lib/navigation.ts index f10e75c1..d0e357ec 100644 --- a/src/lib/navigation.ts +++ b/src/lib/navigation.ts @@ -311,7 +311,7 @@ export const tabNavigation: NavTab[] = [ { title: 'How to guides', items: [ - { title: 'Running Evaluations', href: '/docs/evaluation/features/evaluate' }, + { title: 'Running Evaluations', href: '/docs/evaluation/guides/running-evaluations' }, { title: 'Explore playground', items: [ @@ -321,14 +321,14 @@ export const tabNavigation: NavTab[] = [ { title: 'Eval logs', href: '/docs/evaluation/guides/explore-playground/eval-logs' }, ] }, - { title: 'Create a custom eval', href: '/docs/evaluation/features/custom' }, + { title: 'Create a custom eval', href: '/docs/evaluation/guides/custom-evals' }, { title: 'Build a composite evals', href: '/docs/evaluation/guides/composite-evals' }, { title: 'Set up guardrails', href: '/docs/evaluation/guides/guardrails' }, { title: 'Add ground truth', href: '/docs/evaluation/guides/ground-truth' }, { title: 'Collect feedback', href: '/docs/evaluation/guides/feedback' }, { title: 'Configure connectors', href: '/docs/evaluation/guides/connectors' }, - { title: 'Use custom models', href: '/docs/evaluation/features/custom-models' }, - { title: 'Evaluate in CI/CD', href: '/docs/evaluation/features/cicd' }, + { title: 'Use custom models', href: '/docs/evaluation/guides/custom-models' }, + { title: 'Evaluate in CI/CD', href: '/docs/evaluation/guides/cicd' }, ] }, { diff --git a/src/lib/redirects.ts b/src/lib/redirects.ts index e08d5883..1f09837b 100644 --- a/src/lib/redirects.ts +++ b/src/lib/redirects.ts @@ -154,12 +154,12 @@ export const redirectMap: Record = { '/future-agi/get-started/evaluation/builtin-evals/toxicity': '/docs/evaluation/builtin/toxicity', '/future-agi/get-started/evaluation/builtin-evals/translation-accuracy': '/docs/evaluation/builtin/translation-accuracy', '/future-agi/get-started/evaluation/builtin-evals/valid-links': '/docs/evaluation/builtin', - '/future-agi/get-started/evaluation/create-custom-evals': '/docs/evaluation/features/custom', - '/future-agi/get-started/evaluation/evaluate-ci-cd-pipeline': '/docs/evaluation/features/cicd', - '/future-agi/get-started/evaluation/evaluate-patterns': '/docs/evaluation/features/evaluate', + '/future-agi/get-started/evaluation/create-custom-evals': '/docs/evaluation/guides/custom-evals', + '/future-agi/get-started/evaluation/evaluate-ci-cd-pipeline': '/docs/evaluation/guides/cicd', + '/future-agi/get-started/evaluation/evaluate-patterns': '/docs/evaluation/guides/running-evaluations', '/future-agi/get-started/evaluation/future-agi-models': '/docs/evaluation/concepts/judge-models', - '/future-agi/get-started/evaluation/running-your-first-eval': '/docs/evaluation/features/evaluate', - '/future-agi/get-started/evaluation/use-custom-models': '/docs/evaluation/features/custom-models', + '/future-agi/get-started/evaluation/running-your-first-eval': '/docs/evaluation/guides/running-evaluations', + '/future-agi/get-started/evaluation/use-custom-models': '/docs/evaluation/guides/custom-models', '/future-agi/get-started/knowledge-base/concept': '/docs/knowledge-base/concepts/concept', '/future-agi/get-started/knowledge-base/how-to/create-kb-using-sdk': '/docs/knowledge-base/features/sdk', '/future-agi/get-started/knowledge-base/how-to/create-kb-using-ui': '/docs/knowledge-base/features/ui', diff --git a/src/pages/docs/cookbook/decrease-hallucination.mdx b/src/pages/docs/cookbook/decrease-hallucination.mdx index d4677500..3727b475 100644 --- a/src/pages/docs/cookbook/decrease-hallucination.mdx +++ b/src/pages/docs/cookbook/decrease-hallucination.mdx @@ -592,7 +592,7 @@ The winner configuration was CharacterTextSplitter_mmr_map_rerank, which combine - **Can I create custom evaluations tailored to my RAG use case in Future AGI?** - Yes. The Deterministic Eval template in Future AGI supports custom evaluations (***Click [here](/docs/evaluation/features/custom) to learn more about deterministic eval***). This lets you apply stringent criteria to your RAG outputs minimising variability. + Yes. The Deterministic Eval template in Future AGI supports custom evaluations (***Click [here](/docs/evaluation/guides/custom-evals) to learn more about deterministic eval***). This lets you apply stringent criteria to your RAG outputs minimising variability. ## Ready to Reduce Hallucinations in Your RAG Applications? diff --git a/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx b/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx index b2f46c78..162914d6 100644 --- a/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx +++ b/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx @@ -250,7 +250,7 @@ You ran a built-in eval, found rows where it disagreed with human judgment, enco ## Explore further - + Full reference for the custom eval template API diff --git a/src/pages/docs/dataset/features/experiments.mdx b/src/pages/docs/dataset/features/experiments.mdx index 5b026a59..cd91bea7 100644 --- a/src/pages/docs/dataset/features/experiments.mdx +++ b/src/pages/docs/dataset/features/experiments.mdx @@ -77,7 +77,7 @@ Experiment creation is a guided three-step flow: **Basic Info → Configuration Models you've added through Custom Models show up in the model picker for prompt configurations across all experiment types. - See [Custom Models](/docs/evaluation/features/custom-models) for how to register a custom or self-hosted model. + See [Custom Models](/docs/evaluation/guides/custom-models) for how to register a custom or self-hosted model. @@ -90,7 +90,7 @@ Experiment creation is a guided three-step flow: **Basic Info → Configuration **Compare against baseline (optional)**: pick a column from the dataset to compare model outputs against (typically a ground-truth or existing run-prompt column). Skip it if you don't have a reference output yet; you can still run the experiment, attach evals that don't need a baseline, and add a base column later by editing the experiment. - **Add evaluations**: click **Add Evaluation** and pick from the [built-in eval](/docs/evaluation/builtin) catalog or [create a custom eval](/docs/evaluation/features/custom). Add as many as you need. Every eval runs on every configuration so the results are directly comparable. + **Add evaluations**: click **Add Evaluation** and pick from the [built-in eval](/docs/evaluation/builtin) catalog or [create a custom eval](/docs/evaluation/guides/custom-evals). Add as many as you need. Every eval runs on every configuration so the results are directly comparable. ![Choosing Evals](/screenshot/product/dataset/how-to/experiments-in-dataset/7.png) For each eval, map its inputs (e.g. `output`, `input`, `expected`) to the model output or to dataset columns. Mapping is required before the experiment can run. diff --git a/src/pages/docs/dataset/features/run-prompt.mdx b/src/pages/docs/dataset/features/run-prompt.mdx index 7a09a37e..7583abf3 100644 --- a/src/pages/docs/dataset/features/run-prompt.mdx +++ b/src/pages/docs/dataset/features/run-prompt.mdx @@ -36,28 +36,28 @@ Run Prompt lets you add a new column to your dataset that is filled by a model ( Choose **LLM** for text generation (chat). Use for Q&A, summarization, or any text-in, text-out task. Select a chat model from the list; ensure the provider has an API key configured. ![LLM](/screenshot/product/dataset/how-to/run-prompt-in-dataset/3.png) - Click [here](/docs/evaluation/features/custom-models) to learn how to create custom models. + Click [here](/docs/evaluation/guides/custom-models) to learn how to create custom models. Choose **Text-to-Speech** to generate audio from text. The prompt output column will store audio (e.g. URLs). You can configure voice and format for supported TTS models. ![Text-to-Speech](/screenshot/product/dataset/how-to/run-prompt-in-dataset/4.png) - Click [here](/docs/evaluation/features/custom-models) to learn how to create custom models. + Click [here](/docs/evaluation/guides/custom-models) to learn how to create custom models. Choose **Speech-to-Text** to transcribe audio into text. Use when a column contains audio; the model output will be text in the new column. ![Speech-to-Text](/screenshot/product/dataset/how-to/run-prompt-in-dataset/5.png) - Click [here](/docs/evaluation/features/custom-models) to learn how to create custom models. + Click [here](/docs/evaluation/guides/custom-models) to learn how to create custom models. Choose **Image Generation** to create images from text (or image + text) prompts. The prompt output column will store image URLs. Select an image-generation model and ensure the provider has an API key configured. ![Image Generation](/screenshot/product/dataset/how-to/run-prompt-in-dataset/6.png) - Click [here](/docs/evaluation/features/custom-models) to learn how to create custom models. + Click [here](/docs/evaluation/guides/custom-models) to learn how to create custom models. diff --git a/src/pages/docs/evaluation/concepts/composite-evals.mdx b/src/pages/docs/evaluation/concepts/composite-evals.mdx index 43fe4d96..0ec52136 100644 --- a/src/pages/docs/evaluation/concepts/composite-evals.mdx +++ b/src/pages/docs/evaluation/concepts/composite-evals.mdx @@ -97,10 +97,10 @@ Weights only change the weighted average function; the other four ignore them. P ## Keep exploring - + Run a composite on any surface - + Write a child template of your own diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index 5e36c291..d99df9cb 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -22,7 +22,7 @@ Templates are the reusable unit of evaluation logic. Whether you're checking for See [Built-in evals](/docs/evaluation/builtin) for the full list of available templates. -See [Create custom evals](/docs/evaluation/features/custom) for how to write your own. +See [Create custom evals](/docs/evaluation/guides/custom-evals) for how to write your own. --- @@ -65,7 +65,7 @@ Built-in templates have fixed required keys documented in the template reference ## Next steps - [Built-in evals](/docs/evaluation/builtin): Full list of available templates with required keys and output types. -- [Create custom evals](/docs/evaluation/features/custom): Write your own criteria and rule prompts. +- [Create custom evals](/docs/evaluation/guides/custom-evals): Write your own criteria and rule prompts. - [Eval types](/docs/evaluation/concepts/eval-types): LLM as Judge, Deterministic, Statistical Metric, and LLM as Ranker. - [Judge models](/docs/evaluation/concepts/judge-models): How the model applies a template to produce a result. - [Output types](/docs/evaluation/concepts/output-types): What the output of an eval run looks like diff --git a/src/pages/docs/evaluation/concepts/eval-types.mdx b/src/pages/docs/evaluation/concepts/eval-types.mdx index 62677e6b..a98c9cae 100644 --- a/src/pages/docs/evaluation/concepts/eval-types.mdx +++ b/src/pages/docs/evaluation/concepts/eval-types.mdx @@ -60,7 +60,7 @@ flowchart TD 156 evaluators, each tagged with its type - + Write your own, usually as LLM-as-Judge diff --git a/src/pages/docs/evaluation/concepts/judge-models.mdx b/src/pages/docs/evaluation/concepts/judge-models.mdx index c42940c3..c8aea5ee 100644 --- a/src/pages/docs/evaluation/concepts/judge-models.mdx +++ b/src/pages/docs/evaluation/concepts/judge-models.mdx @@ -40,7 +40,7 @@ Future AGI provides a set of proprietary models built specifically for evaluatio | PROTECT | `protect` | Text, audio | Safety, guardrails, user-defined rules | Low | | PROTECT_FLASH | `protect_flash` | Text | First-pass binary filtering | Ultra-low | -You can also bring your own model using the [custom models](/docs/evaluation/features/custom-models) integration. This is useful when you need a domain-specific fine-tuned model, want to keep inference in a specific cloud region, or already pay for a model you want to use as the judge. +You can also bring your own model using the [custom models](/docs/evaluation/guides/custom-models) integration. This is useful when you need a domain-specific fine-tuned model, want to keep inference in a specific cloud region, or already pay for a model you want to use as the judge. --- @@ -53,12 +53,12 @@ You can also bring your own model using the [custom models](/docs/evaluation/fea | Large-scale runs where speed is important | `turing_flash` | | Safety and guardrail checks | `protect` or `protect_flash` | | Evaluating images or audio | `turing_large` or `turing_small` | -| Domain-specific or compliance requirements | [Custom model](/docs/evaluation/features/custom-models) | +| Domain-specific or compliance requirements | [Custom model](/docs/evaluation/guides/custom-models) | --- ## Next steps -- [Use custom models](/docs/evaluation/features/custom-models): Bring your own model as the judge. +- [Use custom models](/docs/evaluation/guides/custom-models): Bring your own model as the judge. - [Eval templates](/docs/evaluation/concepts/eval-templates): The criteria the judge applies. - [Output types](/docs/evaluation/concepts/output-types): What the judge produces after scoring diff --git a/src/pages/docs/evaluation/concepts/output-types.mdx b/src/pages/docs/evaluation/concepts/output-types.mdx index 47209dbb..e5f914e7 100644 --- a/src/pages/docs/evaluation/concepts/output-types.mdx +++ b/src/pages/docs/evaluation/concepts/output-types.mdx @@ -60,7 +60,7 @@ These aggregates appear in the evaluation summary view and are tracked per eval ## Next steps -- [Evaluate via Platform & SDK](/docs/evaluation/features/evaluate): Run an eval and see results. +- [Evaluate via Platform & SDK](/docs/evaluation/guides/running-evaluations): Run an eval and see results. - [Eval templates](/docs/evaluation/concepts/eval-templates): How templates define what output type a result uses. - [Judge models](/docs/evaluation/concepts/judge-models): How the judge produces the result and reason. -- [CI/CD pipeline](/docs/evaluation/features/cicd): Track results by version across deploys. +- [CI/CD pipeline](/docs/evaluation/guides/cicd): Track results by version across deploys. diff --git a/src/pages/docs/evaluation/features/cicd.mdx b/src/pages/docs/evaluation/guides/cicd.mdx similarity index 98% rename from src/pages/docs/evaluation/features/cicd.mdx rename to src/pages/docs/evaluation/guides/cicd.mdx index 456dd99d..27f00ad8 100644 --- a/src/pages/docs/evaluation/features/cicd.mdx +++ b/src/pages/docs/evaluation/guides/cicd.mdx @@ -1,5 +1,5 @@ --- -title: "Evaluate via CI/CD Pipeline: Automated Quality Checks" +title: "Evaluate in CI/CD" description: "Run Future AGI evaluations in your CI/CD pipeline to assess model performance on every pull request and keep quality checks consistent before deployment." --- @@ -401,16 +401,16 @@ The workflow posts a comment on your PR with the current version identifier and ## Next Steps - + Run a single eval from the UI or SDK. - + Define eval templates to use in your pipeline. Run multiple evals together as a group. - + Bring your own model for evaluations. diff --git a/src/pages/docs/evaluation/features/custom.mdx b/src/pages/docs/evaluation/guides/custom-evals.mdx similarity index 73% rename from src/pages/docs/evaluation/features/custom.mdx rename to src/pages/docs/evaluation/guides/custom-evals.mdx index 0e9802fd..347afaa8 100644 --- a/src/pages/docs/evaluation/features/custom.mdx +++ b/src/pages/docs/evaluation/guides/custom-evals.mdx @@ -1,11 +1,11 @@ --- title: "Create a custom eval" -description: "Write your own eval when no built-in template fits." +description: "Write your own eval when no built-in template fits" --- -Every AI product has its own definition of a good response. When no [built-in template](/docs/evaluation/builtin) fits, a custom eval lets you encode that definition yourself. You write the criteria once, in plain language, and Future AGI scores every response against it, returning a result and a reason for each one. +Every AI product has its own definition of a good response. When no [built-in template](/docs/evaluation/builtin) fits, a custom eval lets you encode that definition yourself: write the criteria once, in plain language, and Future AGI scores every response against it. -Most custom evals are [LLM-as-Judge](/docs/evaluation/concepts/eval-types), where a judge model applies your rule in one pass. Use a Code Eval when the rule is objective and computable, or an Agent Evaluator when one pass will not do. Once created, a custom eval works exactly like a built-in one: use it on a dataset, in a simulation, or from the SDK, by its name. +Most custom evals are [LLM-as-Judge](/docs/evaluation/concepts/eval-types), where a [judge model](/docs/evaluation/concepts/judge-models) applies your rule in one pass and returns a reason with each verdict. Use a Code Eval when the rule is objective and computable, or an Agent Evaluator when one pass won't do. Once created, a custom eval works exactly like a built-in one: use it on a dataset, in a simulation, or from the SDK, by its name. ## When to use @@ -22,13 +22,18 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the - Open your **dataset**, click **Evaluate** in the top-right, then **Add Evaluation**. Select **Create your own eval** to start the custom-eval flow. + Open your **dataset**, click **Evaluate** in the top-right, then **Add Evaluation**. Select **Create your own evals** to start the custom-eval flow. ![Open evaluation creation](/screenshot/product/evaluation/create-custom-evals/1.png) **Name**: unique name for the eval (lowercase letters, numbers, hyphens, and underscores only; cannot start or end with `-` or `_`). Used when you add the eval to a dataset or call it from the SDK. - **Model**: choose **Use Future AGI Models** (e.g. turing_large, turing_flash, turing_small, protect, protect_flash) or **Use other LLMs** (your own or external providers). For model details, see [Future AGI models](/docs/evaluation/concepts/judge-models) and [Use custom models](/docs/evaluation/features/custom-models). + **Model**: pick who applies your rule. This choice is also what decides the eval's type: + + - **Use Future AGI Agents**: a Future AGI model applies the rule (turing_large, turing_flash, turing_small, protect, protect_flash) + - **Use other LLMs**: a model you bring applies the rule; set one up in [Use custom models](/docs/evaluation/guides/custom-models) + - **Function based**: a Code Eval built on an existing function eval. The result is computed by code, so the rule prompt below doesn't apply + ![Configure basic settings](/screenshot/product/evaluation/create-custom-evals/2.png) @@ -36,14 +41,19 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the ![Define evaluation rules](/screenshot/product/evaluation/create-custom-evals/3.png) - **Pass/Fail**: binary result (e.g. 1.0 pass, 0.0 fail). **Percentage (score)**: numeric score between 0 and 100. **Deterministic choices**: categorical result; provide a dict of allowed choices. + Choose what the eval returns: + + - **Pass/Fail**: binary result, `1.0` pass and `0.0` fail + - **Percentage**: a score between 0 and 100 + - **Deterministic choices**: a category label; you define the set of allowed choices + ![Configure output type](/screenshot/product/evaluation/create-custom-evals/4.png) - - **Tags**: for filtering and organization. - - **Description**: shown in the evaluation list. - - **Check Internet**: allow the eval to fetch up-to-date information when needed. - - **Required keys**: list the input variable names the eval expects (e.g. `input`, `output`, `user_query`, `chatbot_response`). + - **Tags**: for filtering and organization + - **Description**: shown in the evaluation list + - **Check Internet**: allow the eval to fetch up-to-date information when needed + - **Required keys**: list the input variable names the eval expects (e.g. `input`, `output`, `user_query`, `chatbot_response`) ![Add optional settings and save](/screenshot/product/evaluation/create-custom-evals/5.png) @@ -51,7 +61,7 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the Click **Create Evaluation**. The new template appears in your evaluation list and can be added to any dataset or called via the SDK using the name you gave it. - In your dataset, click **Evaluate** then **Add Evaluation**, select the custom eval you created, map the **columns** to the rule-prompt variables, then click **Add & Run**. See [Running evaluations on Future AGI](/docs/evaluation/features/evaluate) for the full run flow. + In your dataset, click **Evaluate** then **Add Evaluation**, select the custom eval you created, map the **columns** to the rule-prompt variables, then click **Add & Run**. See [Running evaluations on Future AGI](/docs/evaluation/guides/running-evaluations) for the full run flow. @@ -87,7 +97,7 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the "tags": ["customer-service"], }, ) - print(response.json()) # {"eval_template_id": "..."} + print(response.json()) # {"status": true, "result": {"eval_template_id": "..."}} ``` @@ -120,13 +130,13 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the ## Dive deeper - + Run your custom eval on any surface - + Bring your own judge model - - 156 templates you can use instead + + Run your custom eval on every pull request diff --git a/src/pages/docs/evaluation/features/custom-models.mdx b/src/pages/docs/evaluation/guides/custom-models.mdx similarity index 97% rename from src/pages/docs/evaluation/features/custom-models.mdx rename to src/pages/docs/evaluation/guides/custom-models.mdx index 57d28ad0..a81bc207 100644 --- a/src/pages/docs/evaluation/features/custom-models.mdx +++ b/src/pages/docs/evaluation/guides/custom-models.mdx @@ -1,5 +1,5 @@ --- -title: "Use Custom Models for AI Evaluation in Future AGI" +title: "Use custom models" description: "Use your own or third-party models for evaluations in Future AGI via supported providers or a custom API endpoint with full configuration control." --- @@ -17,7 +17,7 @@ Two ways to connect: - **Custom endpoint**: Connect any model behind an HTTP API, including self-hosted, fine-tuned, or proxy deployments. -Learn how to define eval rules that use your model: [Create custom evals](/docs/evaluation/features/custom). +Learn how to define eval rules that use your model: [Create custom evals](/docs/evaluation/guides/custom-evals). --- @@ -120,10 +120,10 @@ Fields you may see when adding a model (from a provider or custom). **Applies to ## Next Steps - + Run a single eval from the UI or SDK. - + Define eval rules and select your custom model. @@ -132,7 +132,7 @@ Fields you may see when adding a model (from a provider or custom). **Applies to Built-in models available for evals. - + Run evals automatically in your pipeline. diff --git a/src/pages/docs/evaluation/features/evaluate.mdx b/src/pages/docs/evaluation/guides/running-evaluations.mdx similarity index 97% rename from src/pages/docs/evaluation/features/evaluate.mdx rename to src/pages/docs/evaluation/guides/running-evaluations.mdx index f97c711b..372c5b8c 100644 --- a/src/pages/docs/evaluation/features/evaluate.mdx +++ b/src/pages/docs/evaluation/guides/running-evaluations.mdx @@ -1,5 +1,5 @@ --- -title: "Evaluate via Platform and SDK in Future AGI" +title: "Running Evaluations" description: "Run evaluations using the Future AGI platform UI or the Python SDK. Choose individual templates or batch runs for scalable model assessment." --- @@ -53,7 +53,7 @@ Choose **UI** or **SDK** below; each tab has the process in steps. ![Add and run an eval](/screenshot/product/evaluation/evaluate/5.png) - From the Add Evaluation flow, click **Create your own eval** to define a custom template (name, model, rule prompt, output type, and optional settings). After you save it, the new eval appears in the evaluation list and you can add it to your dataset as in the step above. For full details on creating and configuring custom evals, see [Create custom evals](/docs/evaluation/features/custom). + From the Add Evaluation flow, click **Create your own eval** to define a custom template (name, model, rule prompt, output type, and optional settings). After you save it, the new eval appears in the evaluation list and you can add it to your dataset as in the step above. For full details on creating and configuring custom evals, see [Create custom evals](/docs/evaluation/guides/custom-evals). @@ -149,19 +149,19 @@ For more eval templates and Future AGI models, see [Built-in evals](/docs/evalua ## Next Steps - + Define your own eval rules and criteria. Run multiple evals together as a group. - + Bring your own model for evaluations. Built-in models available for evals. - + Run evals automatically in your pipeline. diff --git a/src/pages/docs/evaluation/index.mdx b/src/pages/docs/evaluation/index.mdx index 8a8b3edb..6e9ba388 100644 --- a/src/pages/docs/evaluation/index.mdx +++ b/src/pages/docs/evaluation/index.mdx @@ -3,7 +3,7 @@ title: "Overview" description: "Measure how well your AI achieves the user's goal" --- -Evaluation is Future AGI's quality measurement layer: it scores every response your AI produces against a definition of "good" that you control. +Evaluation is Future AGI's quality measurement layer: it scores every response your AI produces against a definition of "good" that you control. And it's audio native, so voice agents get scored as directly as text ones. ## What is evaluation? @@ -35,12 +35,12 @@ flowchart LR - [Observe](/docs/observe) records what your agent did, and its traces become eval inputs - Evaluate scores each response and shows the result back on the trace - [Optimization](/docs/optimization) consumes the scores to improve the prompt or model -- A threshold turns the score into a merge gate in [CI/CD](/docs/evaluation/features/cicd) before anything ships +- A threshold turns the score into a merge gate in [CI/CD](/docs/evaluation/guides/cicd) before anything ships ## Start here - + Every surface you can run an eval on, and how to get going @@ -82,7 +82,7 @@ Beyond the loop, the same evals run offline: [datasets](/docs/dataset) score row Attach an eval to a project's traces and score new data as it arrives - + Turn an eval threshold into a merge gate that blocks regressions diff --git a/src/pages/docs/faq.mdx b/src/pages/docs/faq.mdx index 123fbbc3..f9fb8744 100644 --- a/src/pages/docs/faq.mdx +++ b/src/pages/docs/faq.mdx @@ -33,7 +33,7 @@ Future AGI has 70+ built-in evaluation templates covering quality, safety, factu **How do I run my first evaluation?** -See [Evaluate via Platform & SDK](/docs/evaluation/features/evaluate) for step-by-step instructions using the UI or Python SDK. +See [Evaluate via Platform & SDK](/docs/evaluation/guides/running-evaluations) for step-by-step instructions using the UI or Python SDK. **How do I evaluate RAG applications?** diff --git a/src/pages/docs/simulation/features/prompt-simulation.mdx b/src/pages/docs/simulation/features/prompt-simulation.mdx index 6a617ba0..a602dd37 100644 --- a/src/pages/docs/simulation/features/prompt-simulation.mdx +++ b/src/pages/docs/simulation/features/prompt-simulation.mdx @@ -159,7 +159,7 @@ Before you start: have a **prompt template** with at least one saved **prompt ve Use AI-powered suggestions to improve your prompt based on simulation results. - + Build evaluations tailored to your specific use case. From dd70be6ee4d83dec783234492d44e7f51f0aa8b0 Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 16:38:28 +0530 Subject: [PATCH 24/42] docs(evaluation): add Connectors concept External tools you attach to an eval so an Agent Evaluator can use them while it judges: built-in tools like the URL checker, plus custom MCP connectors, managed under the model. Connectors are Agent-Evaluator only. Includes an annotated PNG (white oval + vertical arrow per annotation-conventions.md) of where connectors live. 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zn~F8&a!G(ZFM4Dhr^#MHfWi>_;yU>ZeWwH;PAbr4Sj>R&ypM2rvGvz*GA0u`i};J7 z`FTbrq98}db8a^Ma(sL~0z+eCqju_%?%%dA|8~v^E(ze5{8kt$Zeg$QayY7Evv-N2 z)FOr|QzWc=CwaV-lVSCW!+x=tpD&ibH17Mw@WAAqV_}a|Vm*_%Bt#Pn3k#HV(c56m zIMC?;G5NnEHChB-0**Q)BzpOVVz>r(?tFXl_2(~liMMxQWn=Tcvi}f+whxWvJLXXp z`{V&J-lG!Jlk>|dm=}>6j2hj~AK2keVh@tOCwh9v#eWZYeEywkCMFf$$0UAbx6b#I Pqe`)~+nV!}d*pus40R65 literal 0 HcmV?d00001 diff --git a/src/pages/docs/evaluation/concepts/connectors.mdx b/src/pages/docs/evaluation/concepts/connectors.mdx new file mode 100644 index 00000000..0968fa97 --- /dev/null +++ b/src/pages/docs/evaluation/concepts/connectors.mdx @@ -0,0 +1,31 @@ +--- +title: "Connectors" +description: "A connector is an external tool you attach to an eval so the evaluator can use it while it judges, from a built-in URL checker to your own MCP server." +--- + +A connector is an external tool you attach to an eval so the evaluator can use it while it judges. On its own, a judge reasons only over the text you hand it. With a connector, an [Agent Evaluator](/docs/evaluation/concepts/eval-types) can act mid-evaluation: verify a link, look a value up, or call a system of yours before it settles on a verdict. + +Connectors live on the eval. You add one from the menu under the model, and **Manage connectors** shows what is attached. + +The eval's menu under the model showing Connectors, with a built-in URL checker and Manage connectors for adding a custom MCP connector +*Under the model on an eval, the menu holds Connectors: attach a built-in tool like the URL checker, or add your own from Manage connectors* + +## What you can connect + +- **Built-in tools**, like the **URL checker** that confirms a cited link actually resolves +- **Custom MCP connectors**: any tool you expose over the Model Context Protocol can be attached, so the evaluator calls your own service the same way it calls a built-in one + +## When a connector helps + +Connectors are used by **Agent Evaluators**, the eval type that can run multiple turns and use tools. Reach for one when the judgment needs a fact the response does not carry, like confirming a URL is real or checking a value against your own data. A single-pass LLM-as-Judge or a deterministic Code Eval does not use connectors. + +## Keep exploring + + + + Agent Evaluators are the type that can use tools + + + Every surface you can run an eval on + + From 37fec58379170782e527d61eb2278d2f8e8b2abb Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 17:05:28 +0530 Subject: [PATCH 25/42] docs(evaluation): open Eval templates at the first heading and add the versions diagram Open directly at 'What a template holds' with no lead paragraph, add a diagram of the version flow (edit -> new version -> default and pin), link first mentions (dataset, simulation, judge, context, output types), and shorten the description. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../evaluation/concepts/eval-templates.mdx | 20 ++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index cba2d584..d07993f3 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -1,14 +1,14 @@ --- title: "Eval templates & versions" -description: "A template is the reusable definition of what an eval checks. Each version saves a snapshot of it, so an eval keeps giving the same result even after you edit the template." +description: "The reusable definition of what an eval measures, and the versions that keep a run stable" --- -A template is where you define what an eval checks. Whether you check for toxicity, verify a response stays grounded in its source, or enforce a company rule, that logic lives in the template and nowhere else. You write it once and reuse it by name across a dataset, a simulation, a live trace, or a check that runs on every pull request. A template is either built by Future AGI (built-in) or written by you (custom). - ## What a template holds -- **Criteria** the judge applies, written as a rule the model can follow -- **Required inputs**: the keys the template needs, like `output` (the response) and `context` (the source it should stay grounded in) +A template is where you define what an eval checks. You write it once and reuse it by name across a [dataset](/docs/dataset), a [simulation](/docs/simulation), a live trace, or a check that runs on every pull request, and it is either built by Future AGI (built-in) or written by you (custom). A template holds: + +- **Criteria** the [judge](/docs/evaluation/concepts/judge-models) applies, written as a rule the model can follow +- **Required inputs**: the keys the template needs, like `output` (the response) and [`context`](/docs/evaluation/concepts/context) (the source it should stay grounded in) - **Output type**: whether the result is pass or fail, a score, or a category, covered in [Output types](/docs/evaluation/concepts/output-types) - **Pass threshold**: for a score or a category, the line that turns the raw value into a pass or fail - **Reason** (optional): a plain-language explanation of the verdict, when the eval produces one @@ -27,9 +27,15 @@ Here `output` and `context` become the required inputs for that template. ## Versions -Every time you change a template, Future AGI saves the previous one as a numbered version, so nothing you already ran gets overwritten. One version is the **default**: the one a new run uses. +Every time you change a template, Future AGI saves the previous one as a numbered version, so nothing you already ran gets overwritten. One version is the **default**: the one a new run uses. Each version is a frozen copy of the exact criteria and threshold, so you can **pin** a run to a specific version: re-run the eval a month later and it scores the same way, and an eval that gates your pull requests keeps behaving the same even after you edit the template. -Each version is a frozen copy of the exact criteria and threshold. Because of that, you can **pin** a run to a specific version: re-run the eval a month later and it scores the same way, and an eval that gates your pull requests keeps behaving the same even after you edit the template. + V["New version"] + V --> D["Default
a new run reads this"] + V --> P["Pin
frozen for a run or CI gate"] +`} /> ## Single or composite From f934a9cf9f468dd4b0de04dbdec658d55ad2d848 Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 17:06:25 +0530 Subject: [PATCH 26/42] docs(evaluation): open Judge models at the first heading and add the scoring diagram Open directly at 'What a judge does' with no lead paragraph, add a diagram of how a judge scores (response and criteria -> judge -> result and optional reason), link first mentions (template, eval types), soften reason to optional, and shorten the description. Co-Authored-By: Claude Opus 4.8 (1M context) --- src/pages/docs/evaluation/concepts/judge-models.mdx | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/judge-models.mdx b/src/pages/docs/evaluation/concepts/judge-models.mdx index b69ebffb..4bd2f196 100644 --- a/src/pages/docs/evaluation/concepts/judge-models.mdx +++ b/src/pages/docs/evaluation/concepts/judge-models.mdx @@ -1,9 +1,18 @@ --- title: "Judge models" -description: "A judge model reads a response and applies a template's criteria to produce a result and a reason. Future AGI ships judges built for evaluation, and you can bring your own." +description: "The model that reads a response and applies a template's criteria to score it" --- -A judge model is the model that reads a response and applies an eval template's criteria to produce a result and a reason. It receives the text to evaluate, the template's rule, and the required inputs, then returns a verdict. A judge only reads and scores; it never generates or edits your AI's output. Agent Evaluator and LLM-as-Judge evals use a judge; Code Evals do not. +## What a judge does + +A judge model reads a response and applies an eval [template's](/docs/evaluation/concepts/eval-templates) criteria to produce a result, and an optional reason. It receives the text to evaluate, the template's rule, and the required inputs, then returns a verdict. A judge only reads and scores; it never generates or edits your AI's output. [Agent Evaluator and LLM-as-Judge](/docs/evaluation/concepts/eval-types) evals use a judge; Code Evals do not. + + J["Judge model
reads and scores"] + J --> OUT["Result + optional reason"] +`} /> ## Future AGI judge models From d77ec0ac957b9aa48505685041fb418588123709 Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 17:11:21 +0530 Subject: [PATCH 27/42] docs(evaluation): open Context at the first heading and tighten it Open directly at 'What context is' with no lead paragraph, shorten the description, and link the dataset first mention. No diagram (a flow read as forced here). Co-Authored-By: Claude Opus 4.8 (1M context) --- src/pages/docs/evaluation/concepts/context.mdx | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/context.mdx b/src/pages/docs/evaluation/concepts/context.mdx index ab9349d7..413a622f 100644 --- a/src/pages/docs/evaluation/concepts/context.mdx +++ b/src/pages/docs/evaluation/concepts/context.mdx @@ -1,11 +1,11 @@ --- title: "Context" -description: "Context is the grounding you give an eval: the facts or retrieved documents a response should stay true to. It is how groundedness and RAG evals know what correct means." +description: "The grounding a response should stay true to, and how groundedness and RAG evals read it" --- -Context is the grounding you give an eval. It is the facts, retrieved documents, tool outputs, or conversation history that a response should stay true to. Without context, an eval can judge whether an answer reads well; with context, it can judge whether the answer is actually supported by the source it was meant to use. +## What context is -You pass context as an input to the eval, the same way you pass the response. It is a `string` or a `list[string]`: one document, or the set of chunks your retrieval returned for that query. +Context is the grounding you give an eval. It is the facts, retrieved documents, tool outputs, or conversation history that a response should stay true to. Without context, an eval can judge whether an answer reads well; with context, it can judge whether the answer is actually supported by the source it was meant to use. You pass it as an input to the eval, the same way you pass the response: a `string` or a `list[string]`, one document or the set of chunks your retrieval returned for that query. ## Evals that use context @@ -21,7 +21,7 @@ An eval that needs context declares it as a required input. If you do not map it ## Knowledge base and context -A **knowledge base** is the full set of sources your app can retrieve from, like all of your product or support documentation. **Context** is the slice of it that retrieval pulls for a single query. An eval scores against the context that was actually used for that response, not the whole knowledge base, so it measures the retrieval you really ran. +A **knowledge base** is the full set of sources your app can retrieve from, like all of your product or support documentation. **Context** is the subset that retrieval pulls for a single query. An eval scores against the context that was actually used for that response, not the whole knowledge base, so it measures the retrieval you really ran. ## Context in an agent @@ -29,7 +29,7 @@ An agent often retrieves more than once, refining its query as it goes. Each ret ## Providing context -You give an eval its context wherever the eval runs. In a dataset, map the column that holds your context to the template's `context` input. In the SDK, pass it as the `context` input alongside the response. On a traced run, the context arrives in the trace as a span attribute, and you map that attribute to the template's `context` input yourself, since its name depends on how your app is instrumented. +You give an eval its context wherever the eval runs. In a [dataset](/docs/dataset), map the column that holds your context to the template's `context` input. In the SDK, pass it as the `context` input alongside the response. On a traced run, the context arrives in the trace as a span attribute, and you map that attribute to the template's `context` input yourself, since its name depends on how your app is instrumented. ## Keep exploring From 7fe28756444a8c3ef5034f9d32cb545797e704d5 Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 17:11:27 +0530 Subject: [PATCH 28/42] docs(evaluation): open Connectors at the first heading Open directly at 'What a connector is' with no lead paragraph and shorten the description. Keeps the annotated screenshot; no diagram (a flow read as forced). Co-Authored-By: Claude Opus 4.8 (1M context) --- src/pages/docs/evaluation/concepts/connectors.mdx | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/pages/docs/evaluation/concepts/connectors.mdx b/src/pages/docs/evaluation/concepts/connectors.mdx index 0968fa97..cf400bd7 100644 --- a/src/pages/docs/evaluation/concepts/connectors.mdx +++ b/src/pages/docs/evaluation/concepts/connectors.mdx @@ -1,8 +1,10 @@ --- title: "Connectors" -description: "A connector is an external tool you attach to an eval so the evaluator can use it while it judges, from a built-in URL checker to your own MCP server." +description: "External tools an Agent Evaluator can use while it judges, from a built-in URL checker to your own MCP server" --- +## What a connector is + A connector is an external tool you attach to an eval so the evaluator can use it while it judges. On its own, a judge reasons only over the text you hand it. With a connector, an [Agent Evaluator](/docs/evaluation/concepts/eval-types) can act mid-evaluation: verify a link, look a value up, or call a system of yours before it settles on a verdict. Connectors live on the eval. You add one from the menu under the model, and **Manage connectors** shows what is attached. From 4b4a92c83f2290b3847a128510c2fb7936c6e8cd Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Thu, 9 Jul 2026 17:45:28 +0530 Subject: [PATCH 29/42] docs(evaluation): address review on Running Evaluations hub Sync the title to the sidebar label, repoint stale features/ links to guides/, drop the invented stored-baseline claim on the CI/CD row, add the template-to-surfaces fan-out diagram, define offline/online in place, and link the playground row to the Explore playground overview. Signed-off-by: khushalsonawat --- .../evaluation/guides/running-evaluations.mdx | 29 +++++++++++++++---- 1 file changed, 24 insertions(+), 5 deletions(-) diff --git a/src/pages/docs/evaluation/guides/running-evaluations.mdx b/src/pages/docs/evaluation/guides/running-evaluations.mdx index c20c55b4..78962a47 100644 --- a/src/pages/docs/evaluation/guides/running-evaluations.mdx +++ b/src/pages/docs/evaluation/guides/running-evaluations.mdx @@ -1,6 +1,6 @@ --- -title: "Running evaluations on Future AGI" -description: "Where to run evals on Future AGI, and the guide for each surface." +title: "Running Evaluations" +description: "Where to run evals on Future AGI, and the guide for each surface" --- Evaluations have no separate quickstart. An eval scores data, so you run it wherever your data already lives: a dataset, your live traces, a simulation, the playground, the SDK, or a CI job. The template you run is the same on every surface. The surface only changes what you feed it and where the score lands. @@ -9,21 +9,40 @@ This page is the entry point. Pick your surface below and follow its guide. ## Where evals run + D["Dataset"] + T --> TR["Traces"] + T --> S["Simulation"] + T --> P["Playground"] + T --> K["SDK"] + T --> C["CI/CD"] + D --> SC["Comparable scores"] + TR --> SC + S --> SC + P --> SC + K --> SC + C --> SC +`} /> + +In evaluation, **offline** and **online** describe the data, not your connection: an offline run scores data you've already stored (dataset rows, experiment outputs), an online run scores live production traffic as it arrives. + | Surface | Where you run it | Guide | |---|---|---| | Dataset and experiments | Offline, over every row of a dataset | [Run experiments](/docs/dataset/features/experiments) | | Traces | Online, on live spans, traces, and sessions | [Set up evals in Observe](/docs/observe/guides/setup-evals) | | Simulation | Over simulated conversations | [Run a simulation](/docs/simulation/features/run-simulation) | -| Playground | On one sample input, to see the verdict and reason before you commit | In product | +| Playground | On one sample input, to see the verdict and reason before you commit | [Explore playground](/docs/evaluation/guides/explore-playground) | | SDK | Programmatic runs, with local Code Evals that need no API key | [Evaluation SDK](/docs/sdk/evals) | -| CI/CD | On every pull request, against a stored baseline | [Evaluate in CI/CD](/docs/evaluation/features/cicd) | +| CI/CD | On every pull request, gating the merge on eval scores | [Evaluate in CI/CD](/docs/evaluation/guides/cicd) | The online how-to for scoring live traces lives in Observe; this hub only points to it. Everything else runs from its own module, with the same templates and the same scores. ## Dive deeper - + Write your own when no built-in fits From 48a494e62034f4d521d38f5edd286882cec41d65 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Thu, 9 Jul 2026 18:14:22 +0530 Subject: [PATCH 30/42] docs(evaluation): drop the Context concept Context is an input some templates declare (like output or query), not an object in the evaluation model. Remove the page and its sidebar entry; the grounding idea folds into Eval templates & versions later. Signed-off-by: khushalsonawat --- src/lib/navigation.ts | 1 - .../docs/evaluation/concepts/context.mdx | 46 ------------------- 2 files changed, 47 deletions(-) delete mode 100644 src/pages/docs/evaluation/concepts/context.mdx diff --git a/src/lib/navigation.ts b/src/lib/navigation.ts index 6916d0fe..2719b261 100644 --- a/src/lib/navigation.ts +++ b/src/lib/navigation.ts @@ -313,7 +313,6 @@ export const tabNavigation: NavTab[] = [ { title: 'Eval types', href: '/docs/evaluation/concepts/eval-types' }, { title: 'Eval templates & versions', href: '/docs/evaluation/concepts/eval-templates' }, { title: 'Judge models', href: '/docs/evaluation/concepts/judge-models' }, - { title: 'Context', href: '/docs/evaluation/concepts/context' }, { title: 'Connectors', href: '/docs/evaluation/concepts/connectors' }, { title: 'Output types', href: '/docs/evaluation/concepts/output-types' }, { title: 'Error localization', href: '/docs/evaluation/concepts/error-localization' }, diff --git a/src/pages/docs/evaluation/concepts/context.mdx b/src/pages/docs/evaluation/concepts/context.mdx deleted file mode 100644 index 413a622f..00000000 --- a/src/pages/docs/evaluation/concepts/context.mdx +++ /dev/null @@ -1,46 +0,0 @@ ---- -title: "Context" -description: "The grounding a response should stay true to, and how groundedness and RAG evals read it" ---- - -## What context is - -Context is the grounding you give an eval. It is the facts, retrieved documents, tool outputs, or conversation history that a response should stay true to. Without context, an eval can judge whether an answer reads well; with context, it can judge whether the answer is actually supported by the source it was meant to use. You pass it as an input to the eval, the same way you pass the response: a `string` or a `list[string]`, one document or the set of chunks your retrieval returned for that query. - -## Evals that use context - -Context is what the RAG and retrieval family of evals reads: - -- **Groundedness** checks that the response only claims things the context supports -- **Context adherence** checks that the response follows what the context says -- **Context relevance** checks that the retrieved context actually addresses the query -- **Chunk attribution** traces each claim back to the chunk it came from -- **Chunk utilization** checks whether the retrieval returned the chunks the answer needed - -An eval that needs context declares it as a required input. If you do not map it, the eval has nothing to ground against. - -## Knowledge base and context - -A **knowledge base** is the full set of sources your app can retrieve from, like all of your product or support documentation. **Context** is the subset that retrieval pulls for a single query. An eval scores against the context that was actually used for that response, not the whole knowledge base, so it measures the retrieval you really ran. - -## Context in an agent - -An agent often retrieves more than once, refining its query as it goes. Each retrieval hop carries its own context, and the final answer draws on the accumulated set. On a traced agent run, each retrieval hop's context lives on its own retriever span, so once the eval is pointed at the attribute that holds it, both each hop's retrieval quality and the sufficiency of the final context can be scored hop by hop. - -## Providing context - -You give an eval its context wherever the eval runs. In a [dataset](/docs/dataset), map the column that holds your context to the template's `context` input. In the SDK, pass it as the `context` input alongside the response. On a traced run, the context arrives in the trace as a span attribute, and you map that attribute to the template's `context` input yourself, since its name depends on how your app is instrumented. - -## Keep exploring - - - - How a template declares the inputs it needs - - - The RAG and retrieval evals that read context - - - How context-dependent judgments get made - - From e108e868cdc0e83b3f945bf71cc96b3316d22050 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Thu, 9 Jul 2026 19:24:55 +0530 Subject: [PATCH 31/42] docs(evaluation): rewrite Guardrails concept on the settled framing A guardrail is a safety-valve eval that blocks content from reaching the user; the two run paths are the Agent Command Center gateway (built-in, third-party, and native Protect checks) and the Protect SDK. Drop the wrong Protect-gateway naming, the eval-template-reuse claim, the unverified warning-header mechanics, and the material duplicated from the Command Center guardrails page. Add the deliver-or-block mental model diagram. Signed-off-by: khushalsonawat --- .../docs/evaluation/concepts/guardrails.mdx | 84 +++++++------------ 1 file changed, 28 insertions(+), 56 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/guardrails.mdx b/src/pages/docs/evaluation/concepts/guardrails.mdx index cfd19e89..067aa347 100644 --- a/src/pages/docs/evaluation/concepts/guardrails.mdx +++ b/src/pages/docs/evaluation/concepts/guardrails.mdx @@ -1,72 +1,44 @@ --- title: "Guardrails" -description: "Evaluate and enforce safety at runtime on live traffic, not after the fact" +description: "Safety evals that block bad content before it reaches your user" --- -## About +## What a guardrail is -A **guardrail** is an [evaluation](/docs/evaluation) applied inline to live traffic, where an input or output is checked against a rule and an action is taken based on the result, such as blocking or flagging. Unlike batch evals that score responses after they are generated, guardrails run on the request path itself, in the [Protect gateway](/docs/protect), so decisions can be enforced in real time. +A **guardrail** is a safety valve: anything that stands between your AI and your user and can stop bad content from being delivered. On Future AGI, guardrails are a group of evals put to that job, user-facing safety checks like toxicity, sexism, and personal-information detection, run inline so the verdict becomes an action (deliver or block) instead of a stored score. -Guardrails reuse the same eval templates as batch evals (Toxicity, Groundedness, PII Detection, and custom rules), but apply them differently: synchronously at the gateway, with a choice of three outcomes instead of a score. This lets you check both inputs and outputs in one place, while keeping the same eval definitions across your system. +response"] --> G["Guardrail
a safety eval, run inline"] + G --> P["Passes
delivered to your user"] + G --> B["Fails
blocked before delivery"] + style G fill:#2f2f2f,stroke:#ffffff,stroke-width:2px +`} /> ---- - -## How guardrails differ from evals - -Both use the same templates and judge models, but they answer different questions and run at different times. - -| Aspect | Batch eval | Guardrail | -|---|---|---| -| **When** | After the response is generated | Before the request reaches the provider, or before the response is returned | -| **Where** | Dataset, simulation, or CI/CD | Protect gateway (inline request path) | -| **Result** | Score (0–1), category, or pass/fail | Action (block, warn, or log) | -| **Use case** | Measure quality, understand patterns | Enforce policy in production | - ---- - -## Guardrail structure - -A guardrail policy groups one or more checks and controls how they run. - -Each policy sets: +What makes an eval a guardrail isn't the template, it's the job: deciding in real time whether content ships. -- **Checks**: the safety rules to run, each with a type (Toxicity, PII, Prompt Injection, custom, and others) and a threshold -- **Mode**: `enforce` lets checks act on the request, `monitor` records violations without blocking -- **Scope**: where the policy applies, `global` (all keys), `project` (all keys in a project), or `key` (a single virtual key) +## Two ways to run guardrails -Each check runs at a **stage**, `pre` before the provider call or `post` after it, and resolves to an **action** when it triggers. +### Through the gateway ---- - -## Actions - -When a guardrail check triggers (the input or output violates the rule), one of three actions happens: +[Agent Command Center's gateway](/docs/command-center/features/guardrails) guards traffic at the platform level. It ships built-in checks (rule-based and AI-powered), integrates third-party guardrailing systems, and offers Future AGI's [Protect](/docs/protect) models natively. You group checks into policies, choose enforce or monitor mode, and scope them globally, per project, or per key. Nothing in your application code changes; the gateway sits on the request path. -| Action | Behavior | Use case | -|---|---|---| -| **Block** | Reject the request with 403 Forbidden | Enforce hard boundaries, such as never allowing prompt injection attempts | -| **Warn** | Add a warning header to the response, allow it through | Flag suspicious content without blocking, such as high-toxicity responses | -| **Log** | Record the violation in audit logs, no change to request or response | Passive monitoring for compliance or analysis | - ---- +### Through the Protect SDK -## Runtime vs batch +Protect is the in-code path: a small set of guardrailing evals built for millisecond latency, available only through the [SDK's guardrails module](/docs/sdk/evals/guardrails-module). Nothing is on by default; you enable the checks you want at the point where your code produces output, so the screen runs inside your application without adding noticeable turnaround. -Guardrails run at the Protect gateway during request and response processing, so they can make real-time decisions. Batch evals run offline on datasets or after responses are collected. +## Guardrails and standard evals -Running evals at the gateway means: - -- Responses flagged at the input stage never reach the provider -- Outputs flagged at the output stage are blocked before they are returned -- All decisions are audit-logged for compliance -- Latency is predictable and measured in milliseconds - -The trade-off is that guardrails must evaluate quickly. Complex evals that require extensive reasoning may not be practical as guardrails; simpler rule-based checks and fast scanning work best. - ---- +The same eval can hold both jobs. Toxicity is available as a guardrail (inline, blocking) and as a standard eval on the platform, where [judge models](/docs/evaluation/concepts/judge-models) like the Turing family give you deeper analysis over a dataset or your traces. Scoring yesterday's responses for toxicity is evaluation; refusing to deliver today's toxic response is a guardrail. If you don't need live blocking, run the same checks as standard evals and skip the gateway entirely. -## Next steps +## Keep exploring -- [Eval templates](/docs/evaluation/concepts/eval-templates): The checks a guardrail reuses and how to customize them. -- [Evaluate via Platform and SDK](/docs/evaluation/features/evaluate): Run the same templates as batch evals. -- [Protect](/docs/protect): Configure and enforce guardrail policies at the gateway. + + + Policies, built-in checks, and third-party integrations + + + The SDK safety evals built for real-time blocking + + From 59e814538ea09cfbf3de1948b8e120424a927568 Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Thu, 9 Jul 2026 19:39:19 +0530 Subject: [PATCH 32/42] docs(evaluation): address review on Error localization - drop About heading and --- dividers, open at a real first H2 - bold error localization on first mention, link judge model/eval template/Observe - drop the fake configured-threshold framing; state the real pass/fail vs low-score trigger - soften code-type/non-scorable rows to what's grounded, not the private ee engine's internals - compress How to enable it to the checkbox + the real OR-of-two-flags inheritance - call it the Error Localization checkbox, not the API field name - reword Review/Where results appear to the confirmed surfaces (trace detail eval view, dataset row drawer, Playground run tests) - fix stale link to guides/running-evaluations, cut the backward Understanding-evaluation link - Next steps -> Keep exploring, as cards TH-6675 --- .../concepts/error-localization.mdx | 68 +++++++------------ 1 file changed, 24 insertions(+), 44 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/error-localization.mdx b/src/pages/docs/evaluation/concepts/error-localization.mdx index 383e3fcb..86a7b91b 100644 --- a/src/pages/docs/evaluation/concepts/error-localization.mdx +++ b/src/pages/docs/evaluation/concepts/error-localization.mdx @@ -3,64 +3,44 @@ title: "Error localization" description: "Pinpoint which input field caused an evaluation to fail" --- -## About +## Which input caused the failure -When an evaluation fails, error localization narrows down the cause by analyzing which input field led to the failure. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible. This helps you debug faster and see whether the problem is in your data, your retrieval, or your instructions. - ---- +When an evaluation fails, **error localization** narrows down the cause by analyzing which input field led to the failure, using the [judge model](/docs/evaluation/concepts/judge-models)'s explanation and the [eval template](/docs/evaluation/concepts/eval-templates)'s criteria. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible, so you can see whether the problem is in your data, your retrieval, or your instructions. This works the same whether you're testing in the Playground or reviewing a live trace in [Observe](/docs/observe). ## How it works -Error localization runs when an evaluation produces a failed result. The process is: - -1. **Evaluation fails**: the eval score falls below the configured threshold, triggering error localization. +1. **Evaluation fails**: error localization runs when a result comes back failed, pass/fail evals by their own verdict, scored evals on a low score. 2. **Analysis**: an error-localizing agent receives the input data, the eval criteria, the eval result, and the judge's explanation, then works out which input drove the failure. 3. **Result**: you get back an `error_analysis` block describing the problem and a `selected_input_key` naming the input field identified as the issue. -4. **Review**: in the dataset or trace view, the selected field is flagged so you can review it directly. - -The analysis depends on the eval criteria and the judge's explanation, so vague rule prompts produce less precise results. +4. **Review**: the flagged field shows up in the trace detail eval view in Observe, and in the Playground's run tests. ---- - -## When error localization runs +## When it runs -Error localization is selective. It only runs under specific conditions: +Error localization runs on a failed result, as long as the eval isn't a code-type or composite eval and the result can actually be scored. It's skipped when: -| Condition | What happens | -|---|---| -| Evaluation passes | Skipped; there is no error to locate | -| Evaluation fails | Runs to identify the problem | -| Code-type eval | Skipped; not applicable to code logic | -| Composite eval | Not supported yet | -| Non-scorable result | Skipped if the result cannot be converted to a numeric score | - ---- +- Evaluation passed, nothing to localize +- Code-type eval, not supported +- Composite eval, not supported yet +- Missing or non-numeric result, nothing to score ## Where results appear -Error localization results appear wherever evaluations run: - -- **Datasets**: the `selected_input_key` is shown alongside the eval result, so you can see which field was identified as problematic -- **Observe traces**: when you run an eval on a span, the result appears in the eval log attached to that span -- **SDK**: the `error_analysis` and `selected_input_key` fields are returned in the result object - ---- +- **Trace detail eval view**: looking at a span's eval result in Observe shows the flagged input and analysis inline +- **Develop's dataset row drawer**: open a row on a dataset run to see the same breakdown +- **Playground**: the Dataset, Trace, and Simulation test modes render it while you're testing a template +- **SDK**: the result object returns `error_analysis` and `selected_input_key` directly ## How to enable it -Error localization is opt-in on the eval template: - -1. Open an eval template, built-in or custom. -2. Set `error_localizer_enabled` to true on the template. -3. Run an evaluation that uses the template. -4. If a result fails, error localization runs and its output is captured. - -Each eval run inherits the template setting and can override it. - ---- +Turn it on with the Error Localization checkbox when you configure an eval. Set it on the eval template and every run inherits it. Set it on a single run to turn it on even when the template has it off, though a run can't turn it off once the template has it on. See [Running Evaluations](/docs/evaluation/guides/running-evaluations) for the full run flow. -## Next steps +## Keep exploring -- [Evaluate via Platform and SDK](/docs/evaluation/features/evaluate): Run an eval and see error localization results. -- [Eval templates](/docs/evaluation/concepts/eval-templates): Enable error localization when you define a template. -- [Understanding evaluation](/docs/evaluation/concepts/understanding-evaluation): Learn how evaluations work end-to-end. + + + Run an eval and see error localization results + + + Where the Error Localization checkbox lives on a template + + From 8efb9e02f37908f9e5af1bb98239b7efe9868ac3 Mon Sep 17 00:00:00 2001 From: Yash Mohan Date: Thu, 9 Jul 2026 19:42:54 +0530 Subject: [PATCH 33/42] docs(evaluation): make Connectors a proper concept page Add the tool-use mental model (response -> Agent Evaluator -> connector -> verdict) and remove the annotated screenshot, which is how-to material and belongs in the Configure connectors guide, not the concept. 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zn~F8&a!G(ZFM4Dhr^#MHfWi>_;yU>ZeWwH;PAbr4Sj>R&ypM2rvGvz*GA0u`i};J7 z`FTbrq98}db8a^Ma(sL~0z+eCqju_%?%%dA|8~v^E(ze5{8kt$Zeg$QayY7Evv-N2 z)FOr|QzWc=CwaV-lVSCW!+x=tpD&ibH17Mw@WAAqV_}a|Vm*_%Bt#Pn3k#HV(c56m zIMC?;G5NnEHChB-0**Q)BzpOVVz>r(?tFXl_2(~liMMxQWn=Tcvi}f+whxWvJLXXp z`{V&J-lG!Jlk>|dm=}>6j2hj~AK2keVh@tOCwh9v#eWZYeEywkCMFf$$0UAbx6b#I Pqe`)~+nV!}d*pus40R65 diff --git a/src/pages/docs/evaluation/concepts/connectors.mdx b/src/pages/docs/evaluation/concepts/connectors.mdx index cf400bd7..37e42d84 100644 --- a/src/pages/docs/evaluation/concepts/connectors.mdx +++ b/src/pages/docs/evaluation/concepts/connectors.mdx @@ -1,21 +1,27 @@ --- title: "Connectors" -description: "External tools an Agent Evaluator can use while it judges, from a built-in URL checker to your own MCP server" +description: "External tools an Agent Evaluator can call while it judges, from a built-in URL checker to your own MCP server" --- ## What a connector is -A connector is an external tool you attach to an eval so the evaluator can use it while it judges. On its own, a judge reasons only over the text you hand it. With a connector, an [Agent Evaluator](/docs/evaluation/concepts/eval-types) can act mid-evaluation: verify a link, look a value up, or call a system of yours before it settles on a verdict. +A connector is an external tool you attach to an eval so the evaluator can use it while it judges. On its own, a judge reasons only over the text you hand it. With a connector, an [Agent Evaluator](/docs/evaluation/concepts/eval-types) can act mid-judgment: when the verdict needs a fact the response does not carry, it calls the connector, reads what comes back, and only then decides. -Connectors live on the eval. You add one from the menu under the model, and **Manage connectors** shows what is attached. + AE["Agent Evaluator"] + AE -->|"needs an outside fact"| CN["Connector
URL checker or MCP tool"] + CN -->|"result"| AE + AE --> V["Verdict + reason"] +`} /> -The eval's menu under the model showing Connectors, with a built-in URL checker and Manage connectors for adding a custom MCP connector -*Under the model on an eval, the menu holds Connectors: attach a built-in tool like the URL checker, or add your own from Manage connectors* +That loop is the difference: a plain judge scores what it can read, while a connected Agent Evaluator can go and check. ## What you can connect - **Built-in tools**, like the **URL checker** that confirms a cited link actually resolves -- **Custom MCP connectors**: any tool you expose over the Model Context Protocol can be attached, so the evaluator calls your own service the same way it calls a built-in one +- **Custom MCP connectors**: any tool you expose over the Model Context Protocol, so the evaluator calls your own service the same way it calls a built-in one ## When a connector helps @@ -27,7 +33,7 @@ Connectors are used by **Agent Evaluators**, the eval type that can run multiple Agent Evaluators are the type that can use tools - - Every surface you can run an eval on + + The library of evaluators you attach connectors to From a2661b54ea0e025f26473f4b99744a1eb1c13fa2 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Fri, 10 Jul 2026 12:00:54 +0530 Subject: [PATCH 34/42] docs(evaluation): address review on Feedback concept Cut the threshold-calibration section and the SDK feedback pointer, rewrite few-shot injection platform-first with a run-time steering diagram, add the feedback actions, complete the sources to all seven values, and move to the concept shape (opens at the first H2, no separators, Keep exploring cards). Signed-off-by: khushalsonawat --- .../docs/evaluation/concepts/feedback.mdx | 56 +++++++++---------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/feedback.mdx b/src/pages/docs/evaluation/concepts/feedback.mdx index b508a407..27486ba3 100644 --- a/src/pages/docs/evaluation/concepts/feedback.mdx +++ b/src/pages/docs/evaluation/concepts/feedback.mdx @@ -3,14 +3,12 @@ title: "Feedback" description: "Record corrections and human signals on eval results to improve your evaluator over time" --- -## About +## What feedback is -**Feedback** is a human or system signal recorded on an [evaluation](/docs/evaluation) result, marking whether you agree with the judgment. When an eval returns a wrong result, you submit feedback with the correct verdict and an optional explanation. That feedback is stored and used to improve future evals: it drives the correction loop so custom evals learn your domain's definition of quality, and it supplies few-shot examples that steer LLM-based evals toward your standards. +**Feedback** is a human or system signal recorded on an [evaluation](/docs/evaluation) result, marking whether you agree with the judgment. When an eval returns a wrong result, you submit feedback with the correct verdict and an optional explanation. That feedback is stored and used to improve future runs: it steers the judge with examples of what you called right and wrong, and it powers the correction loop that teaches custom evals your domain's definition of quality. Feedback turns scattered disagreements into systematic improvement, so evaluators converge on what your team means by correct. ---- - ## How feedback is captured Each feedback entry records: @@ -23,9 +21,21 @@ Each feedback entry records: | **Source** | Where the eval ran: `dataset`, `trace`, `experiment`, `eval_playground`, `prompt`, `observe`, or `sdk` | | **Source ID** | The row, trace, or item the feedback refers to | -Feedback is stored in the `Feedback` model alongside the [eval template](/docs/evaluation/concepts/eval-templates), the person who submitted it, and the evaluated row, so you can retrieve every correction for a given eval. +Every entry is tied to the [eval template](/docs/evaluation/concepts/eval-templates), the person who submitted it, and the evaluated row, so you can pull up every correction for a given eval. On a dataset eval you also pick what happens next when you submit: retune the eval with your correction, re-run just that row, or re-run the whole dataset. ---- +## How feedback improves the next run + +Corrections don't sit in a log. When you submit feedback on a dataset eval, the platform stores the corrected row against the eval template. Every later run of that eval retrieves the most similar past corrections and hands them to the judge as few-shot examples, so it sees what you called right and wrong before it scores. + +a result"] --> B["Correction stored
against the template"] + B --> C["Next run retrieves
similar corrections"] + C --> D["Judge scores with
your examples in context"] +`} /> + +The template itself never changes: the criteria and version stay put. The steering happens at run time, each time the eval runs. ## The correction loop @@ -33,26 +43,12 @@ The correction loop is the pattern that turns feedback into a better eval: run a 1. **Establish a baseline**: run a built-in eval on a batch of rows and compare its results to your manual verdicts. 2. **Find disagreements**: a disagreement is any row where the eval and your team reach different verdicts. These rows teach the evaluator something new. -3. **Encode corrections**: write a custom eval whose rule prompt states your domain rules and includes a few disagreement rows as few-shot examples for the judge model. +3. **Encode corrections**: write a [custom eval](/docs/evaluation/guides/custom-evals) whose rule prompt states your domain rules and includes a few disagreement rows as few-shot examples for the judge model. 4. **Re-score and measure**: run the new eval on the same batch and measure agreement against your verdicts. 5. **Iterate**: pull a fresh batch, add new examples, and increment the version. Most evals converge in a few rounds. See the [eval correction loop cookbook](/docs/cookbook/evaluation/eval-correction-loop) for a worked end-to-end example. ---- - -## How feedback feeds back into evals - -Feedback improves evals in two ways. - -**Threshold calibration.** For custom evals, corrections are collected in a feedback store. Calibration sweeps threshold values and picks the one that best matches your corrections, tightening the eval's pass and fail boundary. - -**Few-shot injection.** For LLM-based evals, the SDK retrieves similar past corrections and injects them into the judge prompt as examples. Instead of a generic prompt, the judge sees what good and bad look like for your domain before it scores. - -The [SDK feedback reference](/docs/sdk/evals/feedback) covers the collector, calibration, retrieval, and storage options in detail. - ---- - ## Sources Feedback can come from any surface where you evaluate, so you can close the loop wherever the eval ran: @@ -60,15 +56,19 @@ Feedback can come from any surface where you evaluate, so you can close the loop | Source | Comes from | Use case | |---|---|---| | **Dataset** | Dataset column results | Labeling rows where bulk evals got it wrong | -| **Trace** | Live traces in Observe | Marking production responses that were misscored | +| **Traces** | Live traces in Observe | Marking production responses that were misscored | | **Experiment** | Experiment results | Marking experiment outcomes that did not match expectations | | **Eval playground** | The eval tester | Quick feedback on an eval without running it on data | +| **Prompt workbench** | Prompt run results | Correcting scores while iterating on a prompt | | **SDK** | Programmatic collection | Collecting corrections at scale in scripts or notebooks | ---- - -## Next steps +## Keep exploring -- [Eval templates](/docs/evaluation/concepts/eval-templates): Write the rule prompts and criteria that feedback refines. -- [Eval correction loop](/docs/cookbook/evaluation/eval-correction-loop): A worked example of turning corrections into a better eval. -- [SDK feedback reference](/docs/sdk/evals/feedback): Calibration, retrieval, and storage in the SDK. + + + Encode your corrections as rules the judge follows + + + A worked example of turning corrections into a better eval + + From 74a82249bfe4726c0ac524a1ed90c8930a2a5d40 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Fri, 10 Jul 2026 12:35:36 +0530 Subject: [PATCH 35/42] docs(evaluation): rename Judge models to Evaluator models "Judge models" read as the LLM-as-Judge eval type (Rishav's feedback), so the concept is now Evaluator model: page moved to concepts/evaluator-models, term renamed across the evaluation tree, FAQ, nav, and redirect targets; the LLM-as-Judge type name stays. Also addresses review on the page: stale guides/ links repointed, modality line made consistent with the table, first mention bolded, backward card swapped for Guardrails, redundant How to choose section dropped, and "Bring your own LLM" phrasing everywhere. Signed-off-by: khushalsonawat --- src/lib/navigation.ts | 2 +- src/lib/redirects.ts | 2 +- .../evaluation/eval-correction-loop.mdx | 2 +- .../evaluation/concepts/eval-templates.mdx | 6 +- .../docs/evaluation/concepts/eval-types.mdx | 8 +-- .../evaluation/concepts/evaluator-models.mdx | 49 +++++++++++++++ .../docs/evaluation/concepts/feedback.mdx | 10 ++-- .../docs/evaluation/concepts/guardrails.mdx | 2 +- .../docs/evaluation/concepts/judge-models.mdx | 60 ------------------- .../docs/evaluation/concepts/output-types.mdx | 6 +- .../concepts/understanding-evaluation.mdx | 8 +-- src/pages/docs/evaluation/guides/cicd.mdx | 2 +- .../docs/evaluation/guides/custom-evals.mdx | 4 +- .../docs/evaluation/guides/custom-models.mdx | 6 +- .../evaluation/guides/running-evaluations.mdx | 4 +- src/pages/docs/evaluation/index.mdx | 4 +- src/pages/docs/faq.mdx | 2 +- 17 files changed, 83 insertions(+), 94 deletions(-) create mode 100644 src/pages/docs/evaluation/concepts/evaluator-models.mdx delete mode 100644 src/pages/docs/evaluation/concepts/judge-models.mdx diff --git a/src/lib/navigation.ts b/src/lib/navigation.ts index 2719b261..e2eb4322 100644 --- a/src/lib/navigation.ts +++ b/src/lib/navigation.ts @@ -312,7 +312,7 @@ export const tabNavigation: NavTab[] = [ { title: 'Understanding Evaluation', href: '/docs/evaluation/concepts/understanding-evaluation' }, { title: 'Eval types', href: '/docs/evaluation/concepts/eval-types' }, { title: 'Eval templates & versions', href: '/docs/evaluation/concepts/eval-templates' }, - { title: 'Judge models', href: '/docs/evaluation/concepts/judge-models' }, + { title: 'Evaluator models', href: '/docs/evaluation/concepts/evaluator-models' }, { title: 'Connectors', href: '/docs/evaluation/concepts/connectors' }, { title: 'Output types', href: '/docs/evaluation/concepts/output-types' }, { title: 'Error localization', href: '/docs/evaluation/concepts/error-localization' }, diff --git a/src/lib/redirects.ts b/src/lib/redirects.ts index b7e213cd..6980ab33 100644 --- a/src/lib/redirects.ts +++ b/src/lib/redirects.ts @@ -176,7 +176,7 @@ export const redirectMap: Record = { '/future-agi/get-started/evaluation/create-custom-evals': '/docs/evaluation/guides/custom-evals', '/future-agi/get-started/evaluation/evaluate-ci-cd-pipeline': '/docs/evaluation/guides/cicd', '/future-agi/get-started/evaluation/evaluate-patterns': '/docs/evaluation/guides/running-evaluations', - '/future-agi/get-started/evaluation/future-agi-models': '/docs/evaluation/concepts/judge-models', + '/future-agi/get-started/evaluation/future-agi-models': '/docs/evaluation/concepts/evaluator-models', '/future-agi/get-started/evaluation/running-your-first-eval': '/docs/evaluation/guides/running-evaluations', '/future-agi/get-started/evaluation/use-custom-models': '/docs/evaluation/guides/custom-models', '/future-agi/get-started/knowledge-base/concept': '/docs/knowledge-base/concepts/concept', diff --git a/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx b/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx index 162914d6..96e679cf 100644 --- a/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx +++ b/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx @@ -253,7 +253,7 @@ You ran a built-in eval, found rows where it disagreed with human judgment, enco Full reference for the custom eval template API - + Pick the right judge model: turing_small, turing_flash, turing_large diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index d99df9cb..08467662 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -5,7 +5,7 @@ description: "Explains what evaluation templates are, the difference between bui ## About -An eval template is the definition of what to measure. It contains the criteria the judge model will apply to each response and specifies what kind of result to return. You create a template once and reuse it across any dataset, simulation, experiment, or SDK call. +An eval template is the definition of what to measure. It contains the criteria the evaluator model will apply to each response and specifies what kind of result to return. You create a template once and reuse it across any dataset, simulation, experiment, or SDK call. Templates are the reusable unit of evaluation logic. Whether you're checking for toxicity, verifying that a response stays grounded in a source document, or enforcing a company-specific rule, the logic lives in the template. @@ -36,7 +36,7 @@ Every template returns one of three output types: | **Score (percentage)** | Numeric value between 0 and 100 | Groundedness: 87 out of 100 | | **Deterministic choices** | Categorical result from a defined set of options | Tone classification: `formal`, `informal`, `neutral` | -Every result also includes a **reason**: a plain-language explanation of why the judge assigned that result. This makes it possible to understand failures without reviewing each response manually. +Every result also includes a **reason**: a plain-language explanation of why the evaluator model assigned that result. This makes it possible to understand failures without reviewing each response manually. --- @@ -67,5 +67,5 @@ Built-in templates have fixed required keys documented in the template reference - [Built-in evals](/docs/evaluation/builtin): Full list of available templates with required keys and output types. - [Create custom evals](/docs/evaluation/guides/custom-evals): Write your own criteria and rule prompts. - [Eval types](/docs/evaluation/concepts/eval-types): LLM as Judge, Deterministic, Statistical Metric, and LLM as Ranker. -- [Judge models](/docs/evaluation/concepts/judge-models): How the model applies a template to produce a result. +- [Evaluator models](/docs/evaluation/concepts/evaluator-models): How the model applies a template to produce a result. - [Output types](/docs/evaluation/concepts/output-types): What the output of an eval run looks like diff --git a/src/pages/docs/evaluation/concepts/eval-types.mdx b/src/pages/docs/evaluation/concepts/eval-types.mdx index a98c9cae..8e414882 100644 --- a/src/pages/docs/evaluation/concepts/eval-types.mdx +++ b/src/pages/docs/evaluation/concepts/eval-types.mdx @@ -5,7 +5,7 @@ description: "The three eval types: Agent Evaluator, LLM-as-Judge, and Code Eval ## Three ways to reach a verdict -Every eval is authored as one of three types. The type decides how the eval reaches its verdict, what it can reason over, and whether a [judge model](/docs/evaluation/concepts/judge-models) is involved. It's the first choice you make when you create an eval. +Every eval is authored as one of three types. The type decides how the eval reaches its verdict, what it can reason over, and whether an [evaluator model](/docs/evaluation/concepts/evaluator-models) is involved. It's the first choice you make when you create an eval. ### Agent Evaluator @@ -13,7 +13,7 @@ A reasoning evaluator that runs multiple turns and can use tools and knowledge b ### LLM-as-Judge -A judge model reads the response, applies the [template](/docs/evaluation/concepts/eval-templates)'s criteria in a single pass, and returns a result plus a reason. It is the workhorse for subjective, context-dependent quality: safety, tone, faithfulness, instruction adherence, and any custom rule you can write in plain language. +An evaluator model reads the response, applies the [template](/docs/evaluation/concepts/eval-templates)'s criteria in a single pass, and returns a result plus a reason. It is the workhorse for subjective, context-dependent quality: safety, tone, faithfulness, instruction adherence, and any custom rule you can write in plain language. ### Code Eval @@ -42,7 +42,7 @@ flowchart TD Q2 -->|"No"| AE["Agent Evaluator"] `} /> -| Type | Judge model | Returns a reason | +| Type | Evaluator model | Returns a reason | |---|---|---| | Agent Evaluator | Yes | Yes | | LLM-as-Judge | Yes | Yes | @@ -51,7 +51,7 @@ flowchart TD ## Keep exploring - + The models Agent Evaluator and LLM-as-Judge use diff --git a/src/pages/docs/evaluation/concepts/evaluator-models.mdx b/src/pages/docs/evaluation/concepts/evaluator-models.mdx new file mode 100644 index 00000000..35e69186 --- /dev/null +++ b/src/pages/docs/evaluation/concepts/evaluator-models.mdx @@ -0,0 +1,49 @@ +--- +title: "Evaluator models" +description: "The model that reads a response and applies a template's criteria to score it" +--- + +## What an evaluator model does + +An **evaluator model** reads a response and applies an eval [template's](/docs/evaluation/concepts/eval-templates) criteria to produce a result, and an optional reason. It receives the text to evaluate, the template's rule, and the required inputs, then returns a verdict. An evaluator model only reads and scores; it never generates or edits your AI's output. [Agent Evaluator and LLM-as-Judge](/docs/evaluation/concepts/eval-types) evals use an evaluator model; Code Evals do not. + + J["Evaluator model
reads and scores"] + J --> OUT["Result + optional reason"] +`} /> + +## Future AGI evaluator models + +Future AGI ships proprietary models built for evaluation, not for general-purpose chat: + +| Model | Code | Inputs | Best for | Latency | +|---|---|---|---|---| +| TURING_LARGE | `turing_large` | Text, image, audio | Max accuracy, multimodal evals | Higher | +| TURING_SMALL | `turing_small` | Text, image | High fidelity at lower cost | Medium | +| TURING_FLASH | `turing_flash` | Text, image | Fast, high-accuracy evals | Low | +| PROTECT | `protect` | Text, audio | Safety, guardrails, custom rules | Low | +| PROTECT_FLASH | `protect_flash` | Text | First-pass binary filtering | Ultra-low | + +## Bring your own LLM + +You can also use your own model as the evaluator, from OpenAI, Bedrock, SageMaker, Vertex AI, Azure, or a custom endpoint. Reach for a custom model when you need a domain-tuned model, must keep inference in a region, or already pay for a model you want to reuse. See [Use custom models](/docs/evaluation/guides/custom-models). + +## Modality + +An evaluator model can only score inputs it can read. Image evals run on any of the Turing models; audio evals need `turing_large` or `protect`. A text-only model cannot score an image or an audio input, so pick a multimodal one when the eval carries one. + +## Keep exploring + + + + Where the Protect models do their real-time work + + + Bring your own LLM as the evaluator + + + What the evaluator model produces after scoring + + diff --git a/src/pages/docs/evaluation/concepts/feedback.mdx b/src/pages/docs/evaluation/concepts/feedback.mdx index 27486ba3..fe998b1e 100644 --- a/src/pages/docs/evaluation/concepts/feedback.mdx +++ b/src/pages/docs/evaluation/concepts/feedback.mdx @@ -5,7 +5,7 @@ description: "Record corrections and human signals on eval results to improve yo ## What feedback is -**Feedback** is a human or system signal recorded on an [evaluation](/docs/evaluation) result, marking whether you agree with the judgment. When an eval returns a wrong result, you submit feedback with the correct verdict and an optional explanation. That feedback is stored and used to improve future runs: it steers the judge with examples of what you called right and wrong, and it powers the correction loop that teaches custom evals your domain's definition of quality. +**Feedback** is a human or system signal recorded on an [evaluation](/docs/evaluation) result, marking whether you agree with the judgment. When an eval returns a wrong result, you submit feedback with the correct verdict and an optional explanation. That feedback is stored and used to improve future runs: it steers the evaluator model with examples of what you called right and wrong, and it powers the correction loop that teaches custom evals your domain's definition of quality. Feedback turns scattered disagreements into systematic improvement, so evaluators converge on what your team means by correct. @@ -25,14 +25,14 @@ Every entry is tied to the [eval template](/docs/evaluation/concepts/eval-templa ## How feedback improves the next run -Corrections don't sit in a log. When you submit feedback on a dataset eval, the platform stores the corrected row against the eval template. Every later run of that eval retrieves the most similar past corrections and hands them to the judge as few-shot examples, so it sees what you called right and wrong before it scores. +Corrections don't sit in a log. When you submit feedback on a dataset eval, the platform stores the corrected row against the eval template. Every later run of that eval retrieves the most similar past corrections and hands them to the evaluator model as few-shot examples, so it sees what you called right and wrong before it scores. a result"] --> B["Correction stored
against the template"] B --> C["Next run retrieves
similar corrections"] - C --> D["Judge scores with
your examples in context"] + C --> D["Evaluator model scores
with your examples"] `} /> The template itself never changes: the criteria and version stay put. The steering happens at run time, each time the eval runs. @@ -43,7 +43,7 @@ The correction loop is the pattern that turns feedback into a better eval: run a 1. **Establish a baseline**: run a built-in eval on a batch of rows and compare its results to your manual verdicts. 2. **Find disagreements**: a disagreement is any row where the eval and your team reach different verdicts. These rows teach the evaluator something new. -3. **Encode corrections**: write a [custom eval](/docs/evaluation/guides/custom-evals) whose rule prompt states your domain rules and includes a few disagreement rows as few-shot examples for the judge model. +3. **Encode corrections**: write a [custom eval](/docs/evaluation/guides/custom-evals) whose rule prompt states your domain rules and includes a few disagreement rows as few-shot examples for the evaluator model. 4. **Re-score and measure**: run the new eval on the same batch and measure agreement against your verdicts. 5. **Iterate**: pull a fresh batch, add new examples, and increment the version. Most evals converge in a few rounds. @@ -66,7 +66,7 @@ Feedback can come from any surface where you evaluate, so you can close the loop - Encode your corrections as rules the judge follows + Encode your corrections as rules the evaluator model follows A worked example of turning corrections into a better eval diff --git a/src/pages/docs/evaluation/concepts/guardrails.mdx b/src/pages/docs/evaluation/concepts/guardrails.mdx index 067aa347..18b07a4c 100644 --- a/src/pages/docs/evaluation/concepts/guardrails.mdx +++ b/src/pages/docs/evaluation/concepts/guardrails.mdx @@ -30,7 +30,7 @@ Protect is the in-code path: a small set of guardrailing evals built for millise ## Guardrails and standard evals -The same eval can hold both jobs. Toxicity is available as a guardrail (inline, blocking) and as a standard eval on the platform, where [judge models](/docs/evaluation/concepts/judge-models) like the Turing family give you deeper analysis over a dataset or your traces. Scoring yesterday's responses for toxicity is evaluation; refusing to deliver today's toxic response is a guardrail. If you don't need live blocking, run the same checks as standard evals and skip the gateway entirely. +The same eval can hold both jobs. Toxicity is available as a guardrail (inline, blocking) and as a standard eval on the platform, where [evaluator models](/docs/evaluation/concepts/evaluator-models) like the Turing family give you deeper analysis over a dataset or your traces. Scoring yesterday's responses for toxicity is evaluation; refusing to deliver today's toxic response is a guardrail. If you don't need live blocking, run the same checks as standard evals and skip the gateway entirely. ## Keep exploring diff --git a/src/pages/docs/evaluation/concepts/judge-models.mdx b/src/pages/docs/evaluation/concepts/judge-models.mdx deleted file mode 100644 index 4bd2f196..00000000 --- a/src/pages/docs/evaluation/concepts/judge-models.mdx +++ /dev/null @@ -1,60 +0,0 @@ ---- -title: "Judge models" -description: "The model that reads a response and applies a template's criteria to score it" ---- - -## What a judge does - -A judge model reads a response and applies an eval [template's](/docs/evaluation/concepts/eval-templates) criteria to produce a result, and an optional reason. It receives the text to evaluate, the template's rule, and the required inputs, then returns a verdict. A judge only reads and scores; it never generates or edits your AI's output. [Agent Evaluator and LLM-as-Judge](/docs/evaluation/concepts/eval-types) evals use a judge; Code Evals do not. - - J["Judge model
reads and scores"] - J --> OUT["Result + optional reason"] -`} /> - -## Future AGI judge models - -Future AGI ships proprietary models built for evaluation, not for general-purpose chat: - -| Model | Code | Inputs | Best for | Latency | -|---|---|---|---|---| -| TURING_LARGE | `turing_large` | Text, image, audio | Max accuracy, multimodal evals | Higher | -| TURING_SMALL | `turing_small` | Text, image | High fidelity at lower cost | Medium | -| TURING_FLASH | `turing_flash` | Text, image | Fast, high-accuracy evals | Low | -| PROTECT | `protect` | Text, audio | Safety, guardrails, user rules | Low | -| PROTECT_FLASH | `protect_flash` | Text | First-pass binary filtering | Ultra-low | - -## Bring your own judge - -You can also use your own model as the judge, from OpenAI, Bedrock, SageMaker, Vertex AI, Azure, or a custom endpoint. Reach for a custom judge when you need a domain-tuned model, must keep inference in a region, or already pay for a model you want to reuse. See [Use custom models](/docs/evaluation/features/custom-models). - -## Modality - -A judge can only score inputs it can read. Image evals need `turing_large` or `turing_small`; audio evals need `turing_large` or `protect`. A text-only judge cannot score an image or an audio input, so pick a multimodal judge when the eval carries one. - -## How to choose - -| Situation | Judge | -|---|---| -| Maximum accuracy over speed | `turing_large` | -| High quality at a reasonable cost | `turing_small` | -| Large runs where speed matters | `turing_flash` | -| Safety and guardrail checks | `protect` or `protect_flash` | -| Image or audio inputs | a multimodal judge (see Modality) | -| Domain-specific or compliance need | a custom judge | - -## Keep exploring - - - - Which types use a judge, and which run without one - - - Bring your own model as the judge - - - What the judge produces after scoring - - diff --git a/src/pages/docs/evaluation/concepts/output-types.mdx b/src/pages/docs/evaluation/concepts/output-types.mdx index e5f914e7..b8543af6 100644 --- a/src/pages/docs/evaluation/concepts/output-types.mdx +++ b/src/pages/docs/evaluation/concepts/output-types.mdx @@ -5,7 +5,7 @@ description: "What an eval returns: pass/fail, scores, or choices, and where res ## About -Every evaluation run produces a result for each row or call that was scored. A result tells you whether the response passed the criteria, how it scored, and why the judge made that decision. Results are stored alongside your data so you can review them, compare across runs, and track quality over time. +Every evaluation run produces a result for each row or call that was scored. A result tells you whether the response passed the criteria, how it scored, and why the evaluator model made that decision. Results are stored alongside your data so you can review them, compare across runs, and track quality over time. --- @@ -16,7 +16,7 @@ Each individual result has three parts: | Field | Description | |---|---| | **Output** | The result value: `1.0` (pass), `0.0` (fail), a score between 0 and 100, or a category label depending on the template's output type | -| **Reason** | A plain-language explanation from the judge describing why it assigned that result | +| **Reason** | A plain-language explanation from the evaluator model describing why it assigned that result | | **Eval ID** | A unique identifier for the eval run, used to retrieve async results | The reason field is especially useful for diagnosing failures. Instead of reviewing each response manually, you can read the reason to understand exactly what caused a pass or fail judgment. @@ -62,5 +62,5 @@ These aggregates appear in the evaluation summary view and are tracked per eval - [Evaluate via Platform & SDK](/docs/evaluation/guides/running-evaluations): Run an eval and see results. - [Eval templates](/docs/evaluation/concepts/eval-templates): How templates define what output type a result uses. -- [Judge models](/docs/evaluation/concepts/judge-models): How the judge produces the result and reason. +- [Evaluator models](/docs/evaluation/concepts/evaluator-models): How the evaluator model produces the result and reason. - [CI/CD pipeline](/docs/evaluation/guides/cicd): Track results by version across deploys. diff --git a/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx b/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx index 03aefe35..d2c08d3a 100644 --- a/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx +++ b/src/pages/docs/evaluation/concepts/understanding-evaluation.mdx @@ -8,7 +8,7 @@ description: "The four objects behind every eval, and the loop they sit in" You define what "good" means once, and the platform scores every response against that definition. Four objects carry the whole product. Learn these and the rest is detail: - A **[template](/docs/evaluation/concepts/eval-templates)** defines *what* to measure: the criteria, the expected output type, and a pass threshold. Templates are reusable and versioned, and are either built by Future AGI or written by you -- A **config** defines *how* to measure for one run: which [judge model](/docs/evaluation/concepts/judge-models), how your data maps to the template's inputs, and the run settings +- A **config** defines *how* to measure for one run: which [evaluator model](/docs/evaluation/concepts/evaluator-models), how your data maps to the template's inputs, and the run settings - A **run** is one execution: a template plus a config plus one unit of data, from a single row to millions - A **score** is the outcome: a value (pass or fail, a number, or a category), an optional reason, and the metadata to trace it back @@ -31,10 +31,10 @@ Evaluation is the middle of a loop, not a standalone tool. You [observe](/docs/o Every eval is authored as one of three [types](/docs/evaluation/concepts/eval-types): - **Agent Evaluator** reasons over multiple turns and can use tools to reach a judgment -- **LLM-as-Judge** has a judge model read the response and apply the criteria in one pass +- **LLM-as-Judge** has an evaluator model read the response and apply the criteria in one pass - **Code Eval** computes the result in a sandbox, with no model and no API key -The type decides how the verdict is reached and whether a judge model is involved. +The type decides how the verdict is reached and whether an evaluator model is involved. ## Keep exploring @@ -45,7 +45,7 @@ The type decides how the verdict is reached and whether a judge model is involve Criteria, inputs, output types, and version snapshots - + The models that read and score a response diff --git a/src/pages/docs/evaluation/guides/cicd.mdx b/src/pages/docs/evaluation/guides/cicd.mdx index 27f00ad8..3c6f14ce 100644 --- a/src/pages/docs/evaluation/guides/cicd.mdx +++ b/src/pages/docs/evaluation/guides/cicd.mdx @@ -411,6 +411,6 @@ The workflow posts a comment on your PR with the current version identifier and Run multiple evals together as a group. - Bring your own model for evaluations. + Bring your own LLM for evaluations diff --git a/src/pages/docs/evaluation/guides/custom-evals.mdx b/src/pages/docs/evaluation/guides/custom-evals.mdx index 347afaa8..7f04a6f5 100644 --- a/src/pages/docs/evaluation/guides/custom-evals.mdx +++ b/src/pages/docs/evaluation/guides/custom-evals.mdx @@ -5,7 +5,7 @@ description: "Write your own eval when no built-in template fits" Every AI product has its own definition of a good response. When no [built-in template](/docs/evaluation/builtin) fits, a custom eval lets you encode that definition yourself: write the criteria once, in plain language, and Future AGI scores every response against it. -Most custom evals are [LLM-as-Judge](/docs/evaluation/concepts/eval-types), where a [judge model](/docs/evaluation/concepts/judge-models) applies your rule in one pass and returns a reason with each verdict. Use a Code Eval when the rule is objective and computable, or an Agent Evaluator when one pass won't do. Once created, a custom eval works exactly like a built-in one: use it on a dataset, in a simulation, or from the SDK, by its name. +Most custom evals are [LLM-as-Judge](/docs/evaluation/concepts/eval-types), where an [evaluator model](/docs/evaluation/concepts/evaluator-models) applies your rule in one pass and returns a reason with each verdict. Use a Code Eval when the rule is objective and computable, or an Agent Evaluator when one pass won't do. Once created, a custom eval works exactly like a built-in one: use it on a dataset, in a simulation, or from the SDK, by its name. ## When to use @@ -134,7 +134,7 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the Run your custom eval on any surface - Bring your own judge model + Bring your own LLM as the evaluator Run your custom eval on every pull request diff --git a/src/pages/docs/evaluation/guides/custom-models.mdx b/src/pages/docs/evaluation/guides/custom-models.mdx index a81bc207..b362c630 100644 --- a/src/pages/docs/evaluation/guides/custom-models.mdx +++ b/src/pages/docs/evaluation/guides/custom-models.mdx @@ -5,7 +5,7 @@ description: "Use your own or third-party models for evaluations in Future AGI v ## About -Evaluations need a model to act as the judge: to read each response and decide whether it passes, fails, or scores within a range. Custom models let you bring your own judge instead of using Future AGI's built-in models. +Evaluations need a model to act as the evaluator: to read each response and decide whether it passes, fails, or scores within a range. Custom models let you bring your own LLM as the evaluator instead of using Future AGI's built-in models. This matters when you have a model that knows your domain better, when you need inference to stay within a specific cloud provider or region, or when you want to track evaluation costs against a model you already pay for. @@ -23,7 +23,7 @@ Learn how to define eval rules that use your model: [Create custom evals](/docs/ --- ## When to use -- **Control cost and compliance**: Bring your own model and set token costs so evaluation spend is tracked. Keep inference in your chosen region or provider for compliance. +- **Control cost and compliance**: Bring your own LLM and set token costs so evaluation spend is tracked. Keep inference in your chosen region or provider for compliance. - **Evaluate with a fine-tuned or internal model**: Run evals with a model tuned on your domain or hosted in-house by connecting it via the custom endpoint option. - **Unify evals across providers**: Add multiple models and use the same eval templates against each to compare quality or cost. - **Proxy or third-party APIs**: Connect any API-compatible endpoint when it is not one of the built-in providers. @@ -129,7 +129,7 @@ Fields you may see when adding a model (from a provider or custom). **Applies to Run multiple evals together as a group. - + Built-in models available for evals. diff --git a/src/pages/docs/evaluation/guides/running-evaluations.mdx b/src/pages/docs/evaluation/guides/running-evaluations.mdx index 78962a47..b949adcf 100644 --- a/src/pages/docs/evaluation/guides/running-evaluations.mdx +++ b/src/pages/docs/evaluation/guides/running-evaluations.mdx @@ -48,7 +48,7 @@ The online how-to for scoring live traces lives in Observe; this hub only points 156 templates across 14 groups - - Pick a judge model for your evals + + Pick an evaluator model for your evals diff --git a/src/pages/docs/evaluation/index.mdx b/src/pages/docs/evaluation/index.mdx index 6e9ba388..71875e71 100644 --- a/src/pages/docs/evaluation/index.mdx +++ b/src/pages/docs/evaluation/index.mdx @@ -16,7 +16,7 @@ Each eval scores one goal on one metric, for example: - **Safety**: is it free of toxicity, prompt injection, and data leaks - **Tone**: does it speak the way your product should -Every run returns a score (pass/fail, a number, or a category) and, for judged evals, a plain-language reason. Because the definition is fixed, the same bar is applied to every response: scores stay comparable across a dataset, a live trace, or a pull request, and a threshold on that score becomes a decision you can automate instead of a vibe check. +Every run returns a score (pass/fail, a number, or a category) and, when an evaluator model is involved, a plain-language reason. Because the definition is fixed, the same bar is applied to every response: scores stay comparable across a dataset, a live trace, or a pull request, and a threshold on that score becomes a decision you can automate instead of a vibe check. ## The quality loop @@ -66,7 +66,7 @@ A few short pages give you the whole mental model: Agent Evaluator, LLM-as-Judge, and Code Eval, and when to reach for each - + What decides the score, and how to choose one diff --git a/src/pages/docs/faq.mdx b/src/pages/docs/faq.mdx index b606e491..e1ccef7b 100644 --- a/src/pages/docs/faq.mdx +++ b/src/pages/docs/faq.mdx @@ -205,7 +205,7 @@ Check that `FI_API_KEY` and `FI_SECRET_KEY` are set correctly. Verify the instru **Evaluations are failing with "model_name required".** -Some built-in evaluations require a judge model. Pass `model_name="turing_flash"` (or another judge model) in your evaluate call. See [Judge Models](/docs/evaluation/concepts/judge-models). +Some built-in evaluations require an evaluator model. Pass `model_name="turing_flash"` (or another evaluator model) in your evaluate call. See [Evaluator Models](/docs/evaluation/concepts/evaluator-models). **I can't find my API keys.** From 7558af82afb65cf2db3c20d625cb5dba6fcf11b8 Mon Sep 17 00:00:00 2001 From: Abhijai Srivastava Date: Fri, 10 Jul 2026 12:35:41 +0530 Subject: [PATCH 36/42] docs(evaluation): compress enablement into the concept intro per review (error localization) --- src/pages/docs/evaluation/concepts/error-localization.mdx | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/error-localization.mdx b/src/pages/docs/evaluation/concepts/error-localization.mdx index 86a7b91b..9e8b95d4 100644 --- a/src/pages/docs/evaluation/concepts/error-localization.mdx +++ b/src/pages/docs/evaluation/concepts/error-localization.mdx @@ -5,7 +5,7 @@ description: "Pinpoint which input field caused an evaluation to fail" ## Which input caused the failure -When an evaluation fails, **error localization** narrows down the cause by analyzing which input field led to the failure, using the [judge model](/docs/evaluation/concepts/judge-models)'s explanation and the [eval template](/docs/evaluation/concepts/eval-templates)'s criteria. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible, so you can see whether the problem is in your data, your retrieval, or your instructions. This works the same whether you're testing in the Playground or reviewing a live trace in [Observe](/docs/observe). +When an evaluation fails, **error localization** narrows down the cause by analyzing which input field led to the failure, using the [judge model](/docs/evaluation/concepts/judge-models)'s explanation and the [eval template](/docs/evaluation/concepts/eval-templates)'s criteria. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible, so you can see whether the problem is in your data, your retrieval, or your instructions. This works the same whether you're testing in the Playground or reviewing a live trace in [Observe](/docs/observe). You turn it on with the Error Localization checkbox when you configure an eval, on the template or a single run; see [Running Evaluations](/docs/evaluation/guides/running-evaluations) for the flow. ## How it works @@ -30,10 +30,6 @@ Error localization runs on a failed result, as long as the eval isn't a code-typ - **Playground**: the Dataset, Trace, and Simulation test modes render it while you're testing a template - **SDK**: the result object returns `error_analysis` and `selected_input_key` directly -## How to enable it - -Turn it on with the Error Localization checkbox when you configure an eval. Set it on the eval template and every run inherits it. Set it on a single run to turn it on even when the template has it off, though a run can't turn it off once the template has it on. See [Running Evaluations](/docs/evaluation/guides/running-evaluations) for the full run flow. - ## Keep exploring From 29c1324d5b439f5bee18a7f49eb1946a461a27cb Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Fri, 10 Jul 2026 13:36:41 +0530 Subject: [PATCH 37/42] docs(evaluation): address review on Ground truth; retitle nav section to Guides Add the two-jobs mental model (answer key + few-shot calibration), switch to evaluator-model terminology, point the reference-based eval mentions at the Built-in evals reference, state the upload formats, and convert the footer to Keep exploring cards. Rename the Evaluation nav section from How to guides to Guides, matching Observe. Signed-off-by: khushalsonawat --- src/lib/navigation.ts | 2 +- .../docs/evaluation/concepts/ground-truth.mdx | 80 +++++++++---------- 2 files changed, 41 insertions(+), 41 deletions(-) diff --git a/src/lib/navigation.ts b/src/lib/navigation.ts index e2eb4322..f7ad2a8b 100644 --- a/src/lib/navigation.ts +++ b/src/lib/navigation.ts @@ -323,7 +323,7 @@ export const tabNavigation: NavTab[] = [ ] }, { - title: 'How to guides', + title: 'Guides', items: [ { title: 'Running Evaluations', href: '/docs/evaluation/guides/running-evaluations' }, { diff --git a/src/pages/docs/evaluation/concepts/ground-truth.mdx b/src/pages/docs/evaluation/concepts/ground-truth.mdx index 981310ec..6d5fd976 100644 --- a/src/pages/docs/evaluation/concepts/ground-truth.mdx +++ b/src/pages/docs/evaluation/concepts/ground-truth.mdx @@ -1,13 +1,23 @@ --- title: "Ground truth" -description: "Reference data that reference-based evals compare AI outputs against to measure accuracy and correctness" +description: "The known-correct answers an eval compares your AI's output against" --- -## About +## What ground truth is -**Ground truth** is a collection of expected or reference values that reference-based evals compare model outputs against. Instead of measuring subjective qualities like tone or safety, a reference-based eval checks whether the generated output matches the known-correct answer. You upload ground truth as a dataset, then attach it to specific [eval templates](/docs/evaluation/concepts/eval-templates) so those templates can compare each model output to its reference at eval time. +**Ground truth** is a collection of expected or reference values that reference-based evals compare model outputs against. Instead of measuring subjective qualities like tone or safety, a reference-based eval checks whether the generated output matches the known-correct answer. You upload ground truth as a table of rows, attach it to specific [eval templates](/docs/evaluation/concepts/eval-templates), and those templates compare each model output to its reference at eval time. ---- +inputs + correct outputs"] --> M["Mapped to a template
variables + roles"] + M --> C["Compare
output vs the reference"] + M --> F["Few-shot
similar rows shown to the evaluator"] + C --> S["Score"] + F --> S +`} /> + +Read it left to right: one upload, mapped once, working two jobs. The reference gives comparison evals their answer key, and the same rows calibrate the evaluator on judged runs. ## When to use ground truth @@ -19,63 +29,53 @@ Ground truth is the right choice when you have a clear, correct answer to compar - A classification task where the correct label is on file - Any task where correct means matching a specific reference output -If you do not have known-correct answers yet, or correctness is subjective like tone or helpfulness, use a [judge model](/docs/evaluation/concepts/judge-models) eval instead. - ---- +If you do not have known-correct answers yet, or correctness is subjective like tone or helpfulness, let an [evaluator model](/docs/evaluation/concepts/evaluator-models) judge the response directly instead. ## Evals that use ground truth Several built-in templates compare against a reference value: -- [Ground Truth Match](/docs/evaluation/builtin/ground-truth-match): pass or fail on whether the output matches the reference value semantically -- Answer Similarity: a similarity score between the output and a reference answer -- Fuzzy Match: approximate string matching with configurable tolerance -- Retrieval metrics such as Recall@K and Mean Average Precision: measure whether a retrieved set contains the expected items +- [Ground Truth Match](/docs/evaluation/builtin): pass or fail on whether the output matches the reference value semantically +- [Answer Similarity](/docs/evaluation/builtin): a similarity score between the output and a reference answer +- [Fuzzy Match](/docs/evaluation/builtin): approximate string matching with configurable tolerance +- Retrieval metrics such as [Recall@K](/docs/evaluation/builtin) and [Mean Average Precision](/docs/evaluation/builtin): measure whether a retrieved set contains the expected items -Each template documents the input keys it expects. When you attach ground truth to a template, you map your dataset columns to those keys. - ---- +Each template documents the input keys it expects. When you attach ground truth to a template, you map your columns to those keys. ## How ground truth data is structured -Ground truth is stored as a dataset with named columns, where each row is one example. At minimum it needs: +You upload ground truth as a CSV, XLS, XLSX, or JSON file; the platform stores it as rows with named columns. This is its own upload attached to a template, not a [dataset](/docs/dataset) in the Datasets product. At minimum it needs: - **Input columns**: the data the model receives, such as the question column for a question-answering eval -- **Expected output column**: the correct answer the eval compares against. Every row must have one -- **Optional explanation column**: any extra context the eval can use, such as the reasoning behind the answer - -The platform stores the dataset as JSON rows with named columns. - ---- +- **Output column**: the correct answer the eval compares against. Every row must have one +- **Optional explanation column**: the reasoning behind the answer, shown to the evaluator alongside it ## Mapping ground truth to eval inputs When you attach ground truth to an eval template, you configure two mappings: -**Variable mapping** connects each eval template input to a ground truth column. For example, if the template needs a `question` input, you map it to the `prompt` column. At eval time the platform uses this mapping to pull the right column value for each row. - -**Role mapping** designates which column is the expected output, and optionally which column carries an explanation. It tells the eval which ground truth value to compare against. - -The platform validates these mappings when you save them, so every mapped column must exist in your dataset. - ---- +- **Variable mapping** connects each eval template input to a ground truth column. If the template needs a `question` input, you map it to your `prompt` column. At eval time the platform uses this mapping to pull the right column value for each row, and it is also what similarity retrieval matches on +- **Role mapping** labels the semantic parts: which column is the correct **output** (required), and which column carries the **explanation** behind it (optional) ## Retrieval and few-shot injection -Ground truth can also calibrate the judge by injecting similar examples into the eval prompt, called **few-shot grounding**. When enabled, the platform: - -1. Embeds your ground truth dataset so similar examples can be found -2. At eval runtime, finds the ground truth rows most similar to the current input -3. Includes those rows in the judge prompt as reference calibration +Ground truth can also calibrate the evaluator by injecting similar examples into its prompt, called **few-shot grounding**. When enabled, the platform: -This helps the judge apply your correctness criteria without you rewriting the prompt. +1. Embeds your ground truth rows so similar examples can be found +2. At eval runtime, finds the rows most similar to the current input (three by default) +3. Includes those rows in the evaluator prompt as reference calibration -Retrieval requires the embedding status to be `Completed`. The platform embeds ground truth asynchronously after you save a mapping, and once embedding finishes, retrieval starts on the next eval run. If you change the variable mapping, the embeddings are marked stale and re-embedded automatically. +This helps the evaluator apply your correctness criteria without you rewriting the prompt. ---- +The platform embeds ground truth asynchronously after you save a mapping, and retrieval starts on the next eval run once embedding completes. If you change the variable mapping, the embeddings are marked stale and re-embedded automatically. -## Next steps +## Keep exploring -- [Ground Truth Match](/docs/evaluation/builtin/ground-truth-match): The built-in eval that compares output to a reference value. -- [Evaluate via Platform and SDK](/docs/evaluation/features/evaluate): Attach ground truth and run evals against it. -- [Judge models](/docs/evaluation/concepts/judge-models): Use a judge when there is no fixed correct answer. + + + The reference-based templates, with the inputs each one expects + + + Attach ground truth and run evals against it on any surface + + From 1d7c1f6bae823274525f558e90e0b5c941a230b9 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Fri, 10 Jul 2026 14:00:20 +0530 Subject: [PATCH 38/42] docs(evaluation): address review on Eval templates & versions Add the templates-and-configs mental model (template as the image, config as a running instance, an "Eval" colloquially meaning a config), repoint judge-models to evaluator-models, fold the grounding clause in place of the dropped Context page, correct the built-in count to 156, and fix a nonexistent card icon. Signed-off-by: khushalsonawat --- .../evaluation/concepts/eval-templates.mdx | 47 ++++++++++--------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index c9b1ac7a..db683089 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -5,15 +5,28 @@ description: "The reusable definition of what an eval measures, and the versions ## What a template holds -A template is where you define what an eval checks. You write it once and reuse it by name across a [dataset](/docs/dataset), a [simulation](/docs/simulation), a live trace, or a check that runs on every pull request, and it is either built by Future AGI (built-in) or written by you (custom). A template holds: +A **template** is where you define what an eval checks. You write it once and reuse it by name across a [dataset](/docs/dataset), a [simulation](/docs/simulation), a live trace, or a check that runs on every pull request, and it is either built by Future AGI (built-in) or written by you (custom). A template holds: -- **Criteria** the [judge](/docs/evaluation/concepts/judge-models) applies, written as a rule the model can follow -- **Required inputs**: the keys the template needs, like `output` (the response) and [`context`](/docs/evaluation/concepts/context) (the source it should stay grounded in) +- **Criteria** the [evaluator model](/docs/evaluation/concepts/evaluator-models) applies, written as a rule the model can follow +- **Required inputs**: the keys the template needs, like `output` (the response) and `context` (the retrieved source the response should stay grounded in, not your whole knowledge base) - **Output type**: whether the result is pass or fail, a score, or a category, covered in [Output types](/docs/evaluation/concepts/output-types) - **Pass threshold**: for a score or a category, the line that turns the raw value into a pass or fail - **Reason** (optional): a plain-language explanation of the verdict, when the eval produces one ---- +## Templates and configs + +Think of a template as a Docker image and an **eval config** as a container. The template is the frozen definition; a config is a running instance of it, pointed at one place your data lives. You can create as many configs of a template as you want: one mapped to a dataset's columns, one attached to a project's live traces, one in a CI job, all applying the same definition. + +the image: criteria, inputs, output type"] --> C1["Config
mapped to a dataset"] + T --> C2["Config
on a project's traces"] + T --> C3["Config
in a CI job"] + style T fill:#2f2f2f,stroke:#ffffff,stroke-width:2px +`} /> + +Each config runs at its module's level, and a config is what the platform loosely calls an "Eval". When someone says an eval ran, a config ran. ## Built-in vs custom templates @@ -21,18 +34,14 @@ A template is where you define what an eval checks. You write it once and reuse |---|---|---| | **Who writes the criteria** | Future AGI | You | | **How to access** | Select from the template list in the UI or pass the name to the SDK | Create via UI or API, then use by name | -| **Covers** | 70+ categories: quality, safety, factuality, RAG, bias, format, audio, image | Any domain-specific, business, or regulatory rule you define | +| **Covers** | 156 templates across 14 groups: quality, safety, factuality, RAG, bias, format, audio, image | Any domain-specific, business, or regulatory rule you define | | **Required inputs** | Defined per template (e.g. `input`, `output`, `context`) | You define the required keys in the template config | -See [Built-in evals](/docs/evaluation/builtin) for the full list of available templates. - -See [Create custom evals](/docs/evaluation/guides/custom-evals) for how to write your own. - ---- +[Built-in evals](/docs/evaluation/builtin) lists every template; [Create a custom eval](/docs/evaluation/guides/custom-evals) shows how to write your own. ## Required inputs and mapping -A template declares the input keys it needs, and at run time you map your real data to those keys. A groundedness template, for example, needs `output` and `context`; you point each one at the right column or field. +A template declares the input keys it needs, and at run time a config maps your real data to those keys. A groundedness template, for example, needs `output` and `context`; you point each one at the right column or field. Custom templates define their own keys with `{{variable}}` placeholders in the rule prompt. The names you write become the inputs you must supply: @@ -46,26 +55,18 @@ Here `output` and `context` become the required inputs for that template. Every time you change a template, Future AGI saves the previous one as a numbered version, so nothing you already ran gets overwritten. One version is the **default**: the one a new run uses. Each version is a frozen copy of the exact criteria and threshold, so you can **pin** a run to a specific version: re-run the eval a month later and it scores the same way, and an eval that gates your pull requests keeps behaving the same even after you edit the template. - V["New version"] - V --> D["Default
a new run reads this"] - V --> P["Pin
frozen for a run or CI gate"] -`} /> - ## Single or composite -A template is **single** (it runs on its own) or **composite** (it aggregates several child templates into one score). Reach for a composite when "good" means several checks at once, like faithful and on-tone and complete. +A template is **single** (it runs on its own) or **[composite](/docs/evaluation/concepts/composite-evals)** (it aggregates several child templates into one score). Reach for a composite when "good" means several checks at once, like faithful and on-tone and complete. ## Keep exploring - + Pass or fail, score, and category, and how a threshold decides - - The grounding you pass as the context input + + The models that apply a template's criteria 156 ready templates, each with its inputs and output type From 5d062016060fe8be2767971cfb500ea21c5d02a5 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Fri, 10 Jul 2026 14:31:56 +0530 Subject: [PATCH 39/42] docs(evaluation): swap the Docker analogy for a plain template-config statement Signed-off-by: khushalsonawat --- src/pages/docs/evaluation/concepts/eval-templates.mdx | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index db683089..5cbb9a09 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -15,14 +15,14 @@ A **template** is where you define what an eval checks. You write it once and re ## Templates and configs -Think of a template as a Docker image and an **eval config** as a container. The template is the frozen definition; a config is a running instance of it, pointed at one place your data lives. You can create as many configs of a template as you want: one mapped to a dataset's columns, one attached to a project's live traces, one in a CI job, all applying the same definition. +A template is the definition, written once. An **eval config** is that definition pointed at one place your data lives, and you can create as many as you want: one mapped to a dataset's columns, one attached to a project's live traces, one in a CI job. the image: criteria, inputs, output type"] --> C1["Config
mapped to a dataset"] - T --> C2["Config
on a project's traces"] - T --> C3["Config
in a CI job"] + T["Template
the definition: criteria, inputs, output type"] --> C1["Config
mapped to dataset columns"] + T --> C2["Config
mapped to span attributes"] + T --> C3["Config
mapped to a CI job's outputs"] style T fill:#2f2f2f,stroke:#ffffff,stroke-width:2px `} /> From 77288903f57266a14edc04fed1c0c8dcb57ec331 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Fri, 10 Jul 2026 14:49:36 +0530 Subject: [PATCH 40/42] Restructuring the pages --- src/lib/navigation.ts | 208 +++++++++++++----- .../docs/evaluation/concepts/connectors.mdx | 39 ---- .../evaluation/guides/using-connectors.mdx | 54 +++++ 3 files changed, 210 insertions(+), 91 deletions(-) delete mode 100644 src/pages/docs/evaluation/concepts/connectors.mdx create mode 100644 src/pages/docs/evaluation/guides/using-connectors.mdx diff --git a/src/lib/navigation.ts b/src/lib/navigation.ts index f10e75c1..fb71b188 100644 --- a/src/lib/navigation.ts +++ b/src/lib/navigation.ts @@ -60,13 +60,28 @@ export const tabNavigation: NavTab[] = [ title: 'Self-Hosting', items: [ { title: 'Overview', href: '/docs/self-hosting' }, - { title: 'System requirements', href: '/docs/self-hosting/requirements' }, - { title: 'Environment variables', href: '/docs/self-hosting/environment' }, - { title: 'Configuration', href: '/docs/self-hosting/configuration' }, - { title: 'Docker Compose', href: '/docs/self-hosting/docker-compose' }, - { title: 'Production', href: '/docs/self-hosting/production' }, - { title: 'User management', href: '/docs/self-hosting/user-management' }, - { title: 'Troubleshooting and FAQs', href: '/docs/self-hosting/troubleshooting' }, + { title: 'Requirements', href: '/docs/self-hosting/requirements' }, + { title: 'Installation', href: '/docs/self-hosting/installation' }, + { + title: 'Configuration', + items: [ + { title: 'System configuration', href: '/docs/self-hosting/configuration/system' }, + { title: 'Environment variables', href: '/docs/self-hosting/configuration/environment' }, + ] + }, + { + title: 'Production', + items: [ + { title: 'Overview', href: '/docs/self-hosting/production' }, + { title: 'Checklist', href: '/docs/self-hosting/production/checklist' }, + { title: 'Security & TLS', href: '/docs/self-hosting/production/security-tls' }, + { title: 'Backups & restore', href: '/docs/self-hosting/production/backups-restore' }, + { title: 'Monitoring', href: '/docs/self-hosting/production/monitoring' }, + { title: 'Upgrades & rollback', href: '/docs/self-hosting/production/upgrades-rollback' }, + ] + }, + { title: 'Troubleshooting & FAQs', href: '/docs/self-hosting/troubleshooting' }, + { title: 'Support', href: '/docs/self-hosting/support' }, ] }, { @@ -297,9 +312,7 @@ export const tabNavigation: NavTab[] = [ { title: 'Understanding Evaluation', href: '/docs/evaluation/concepts/understanding-evaluation' }, { title: 'Eval types', href: '/docs/evaluation/concepts/eval-types' }, { title: 'Eval templates & versions', href: '/docs/evaluation/concepts/eval-templates' }, - { title: 'Judge models', href: '/docs/evaluation/concepts/judge-models' }, - { title: 'Context', href: '/docs/evaluation/concepts/context' }, - { title: 'Connectors', href: '/docs/evaluation/concepts/connectors' }, + { title: 'Evaluator models', href: '/docs/evaluation/concepts/evaluator-models' }, { title: 'Output types', href: '/docs/evaluation/concepts/output-types' }, { title: 'Error localization', href: '/docs/evaluation/concepts/error-localization' }, { title: 'Guardrails', href: '/docs/evaluation/concepts/guardrails' }, @@ -309,9 +322,9 @@ export const tabNavigation: NavTab[] = [ ] }, { - title: 'How to guides', + title: 'Guides', items: [ - { title: 'Running Evaluations', href: '/docs/evaluation/features/evaluate' }, + { title: 'Running Evaluations', href: '/docs/evaluation/guides/running-evaluations' }, { title: 'Explore playground', items: [ @@ -321,14 +334,14 @@ export const tabNavigation: NavTab[] = [ { title: 'Eval logs', href: '/docs/evaluation/guides/explore-playground/eval-logs' }, ] }, - { title: 'Create a custom eval', href: '/docs/evaluation/features/custom' }, + { title: 'Create a custom eval', href: '/docs/evaluation/guides/custom-evals' }, { title: 'Build a composite evals', href: '/docs/evaluation/guides/composite-evals' }, { title: 'Set up guardrails', href: '/docs/evaluation/guides/guardrails' }, { title: 'Add ground truth', href: '/docs/evaluation/guides/ground-truth' }, { title: 'Collect feedback', href: '/docs/evaluation/guides/feedback' }, - { title: 'Configure connectors', href: '/docs/evaluation/guides/connectors' }, - { title: 'Use custom models', href: '/docs/evaluation/features/custom-models' }, - { title: 'Evaluate in CI/CD', href: '/docs/evaluation/features/cicd' }, + { title: 'Using Connectors', href: '/docs/evaluation/guides/using-connectors' }, + { title: 'Use custom models', href: '/docs/evaluation/guides/custom-models' }, + { title: 'Evaluate in CI/CD', href: '/docs/evaluation/guides/cicd' }, ] }, { @@ -413,7 +426,6 @@ export const tabNavigation: NavTab[] = [ }, { title: 'Setup alerts', href: '/docs/observe/guides/setup-alerts' }, { title: 'Setup evals', href: '/docs/observe/guides/setup-evals' }, - { title: 'Explore sessions & users', href: '/docs/observe/features/session' }, ] }, { @@ -857,7 +869,7 @@ export const tabNavigation: NavTab[] = [ title: 'Observability', icon: 'eye', items: [ - { title: 'Improving a LangGraph agent via observability', href: '/docs/cookbook/improve-langgraph-agent-with-observability' }, + { title: 'Observing a LangGraph agent and obtaining insights', href: '/docs/cookbook/observe-langgraph-agent-and-obtain-insights' }, { title: 'Text-to-SQL Evaluation', href: '/docs/cookbook/text-to-sql' }, ] }, @@ -908,50 +920,142 @@ export const tabNavigation: NavTab[] = [ href: '/docs/sdk', groups: [ { - group: 'SDK Reference', + group: 'SDK Overview', + icon: 'code', items: [ { title: 'SDK Overview', href: '/docs/sdk' }, { - title: 'AI Evaluation', + title: 'List of SDKs', items: [ - { title: 'Overview', href: '/docs/sdk/evals' }, - { title: 'Running Evaluations', href: '/docs/sdk/evals/evaluate' }, - { title: 'Distributed Evaluator', href: '/docs/sdk/evals/distributed' }, - { title: 'AutoEval', href: '/docs/sdk/evals/autoeval' }, - { title: 'Guardrails', href: '/docs/sdk/evals/guardrails-module' }, - { title: 'Local & Hybrid', href: '/docs/sdk/evals/local' }, - { title: 'OpenTelemetry', href: '/docs/sdk/evals/otel' }, - { title: 'Code Security', href: '/docs/sdk/evals/code-security' }, - { - title: 'Metrics Reference', - items: [ - { title: 'Overview', href: '/docs/sdk/evals/metrics' }, - { title: 'String & Similarity', href: '/docs/sdk/evals/metrics/string' }, - { title: 'JSON & Structured', href: '/docs/sdk/evals/metrics/json' }, - { title: 'Hallucination', href: '/docs/sdk/evals/metrics/hallucination' }, - { title: 'RAG', href: '/docs/sdk/evals/metrics/rag' }, - { title: 'Agents & Functions', href: '/docs/sdk/evals/metrics/agents' }, - { title: 'Guardrails', href: '/docs/sdk/evals/metrics/guardrails' }, - ] - }, - { title: 'Cloud Evals', href: '/docs/sdk/evals/cloud-evals' }, - { title: 'LLM-as-Judge', href: '/docs/sdk/evals/llm-judge' }, - { title: 'Streaming', href: '/docs/sdk/evals/streaming' }, - { title: 'Feedback Loops', href: '/docs/sdk/evals/feedback' }, + { title: 'Evaluation', href: '/docs/sdk/list/evaluation' }, + { title: 'TraceAI', href: '/docs/sdk/list/traceai' }, + { title: 'Core SDKs', href: '/docs/sdk/list/core' }, ] }, + ] + }, + { + group: 'Evaluation', + icon: 'chart', + items: [ + { title: 'Overview', href: '/docs/sdk/evals' }, + { title: 'Running Evaluations', href: '/docs/sdk/evals/evaluate' }, + { title: 'AutoEval', href: '/docs/sdk/evals/autoeval' }, + { title: 'LLM-as-Judge', href: '/docs/sdk/evals/llm-judge' }, + { title: 'Guardrails', href: '/docs/sdk/evals/guardrails-module' }, + { title: 'Local & Hybrid', href: '/docs/sdk/evals/local' }, + { title: 'Distributed Evaluator', href: '/docs/sdk/evals/distributed' }, + { title: 'Streaming', href: '/docs/sdk/evals/streaming' }, + { title: 'Cloud Evals', href: '/docs/sdk/evals/cloud-evals' }, + { title: 'Feedback Loops', href: '/docs/sdk/evals/feedback' }, + { title: 'Code Security', href: '/docs/sdk/evals/code-security' }, + { title: 'OpenTelemetry', href: '/docs/sdk/evals/otel' }, + { + title: 'Metrics Reference', + items: [ + { title: 'Overview', href: '/docs/sdk/evals/metrics' }, + { title: 'String & Similarity', href: '/docs/sdk/evals/metrics/string' }, + { title: 'JSON & Structured', href: '/docs/sdk/evals/metrics/json' }, + { title: 'Hallucination', href: '/docs/sdk/evals/metrics/hallucination' }, + { title: 'RAG', href: '/docs/sdk/evals/metrics/rag' }, + { title: 'Agents & Functions', href: '/docs/sdk/evals/metrics/agents' }, + { title: 'Guardrails', href: '/docs/sdk/evals/metrics/guardrails' }, + ] + }, + ] + }, + { + group: 'traceAI', + icon: 'eye', + items: [ + { title: 'Overview', href: '/docs/sdk/tracing' }, { - title: 'Core SDK', + title: 'How-to guides', items: [ - { title: 'Datasets', href: '/docs/sdk/datasets' }, - { title: 'Tracing', href: '/docs/sdk/tracing' }, - { title: 'Protect', href: '/docs/sdk/protect' }, - { title: 'Knowledge Base', href: '/docs/sdk/knowledgebase' }, - { title: 'Annotation Queues', href: '/docs/sdk/annotation-queues' }, - { title: 'Prompt Optimization', href: '/docs/sdk/optimization' }, - { title: 'Simulation Testing', href: '/docs/sdk/simulate' }, + { title: 'Set up tracing', href: '/docs/sdk/tracing/set-up-tracing' }, + { title: 'Instrument with helpers', href: '/docs/sdk/tracing/instrument-with-traceai-helpers' }, + { title: 'Set session & user IDs', href: '/docs/sdk/tracing/set-session-user-id' }, + { title: 'Attributes, metadata & tags', href: '/docs/sdk/tracing/add-attributes-metadata-tags' }, + { title: 'Log prompt templates', href: '/docs/sdk/tracing/log-prompt-templates' }, + { title: 'Events, exceptions & status', href: '/docs/sdk/tracing/add-events-exceptions-status' }, + { title: 'Mask attributes', href: '/docs/sdk/tracing/mask-span-attributes' }, + { title: 'Create tool spans', href: '/docs/sdk/tracing/create-tool-spans' }, + { title: 'Get span context', href: '/docs/sdk/tracing/get-current-span-context' }, + { title: 'In-line evals', href: '/docs/sdk/tracing/in-line-evals' }, + { title: 'Annotate via API', href: '/docs/sdk/tracing/annotating-using-api' }, + { title: 'Advanced examples', href: '/docs/sdk/tracing/advanced-tracing-examples' }, + { title: 'Langfuse integration', href: '/docs/sdk/tracing/langfuse-integration' }, ] }, + { + title: 'Reference', + items: [ + { title: 'register()', href: '/docs/sdk/tracing/register' }, + { title: 'FITracer & custom spans', href: '/docs/sdk/tracing/fitracer' }, + { title: 'Context helpers', href: '/docs/sdk/tracing/context-helpers' }, + { title: 'TraceConfig', href: '/docs/sdk/tracing/trace-config' }, + { title: 'EvalTags', href: '/docs/sdk/tracing/eval-tags' }, + { title: 'Instrumentors', href: '/docs/sdk/tracing/instrumentors' }, + { title: 'Environment variables', href: '/docs/sdk/tracing/environment-variables' }, + { title: 'Semantic conventions', href: '/docs/sdk/tracing/semantic-conventions' }, + ] + }, + ] + }, + { + group: 'Simulation', + icon: 'play', + items: [ + { title: 'Overview', href: '/docs/sdk/simulate' }, + ] + }, + { + group: 'Datasets', + icon: 'table', + items: [ + { title: 'Overview', href: '/docs/sdk/datasets' }, + ] + }, + { + group: 'Prompt Optimization', + icon: 'gauge', + items: [ + { title: 'Overview', href: '/docs/sdk/optimization' }, + ] + }, + { + group: 'Annotation Queues', + icon: 'pen', + items: [ + { title: 'Overview', href: '/docs/sdk/annotation-queues' }, + { + title: 'Concepts', + items: [ + { title: 'Labels', href: '/docs/sdk/annotation-queues/labels' }, + { title: 'Queue management', href: '/docs/sdk/annotation-queues/queues' }, + { title: 'Queue lifecycle', href: '/docs/sdk/annotation-queues/lifecycle' }, + { title: 'Queue items', href: '/docs/sdk/annotation-queues/items' }, + { title: 'Annotations', href: '/docs/sdk/annotation-queues/annotations' }, + { title: 'Scores', href: '/docs/sdk/annotation-queues/scores' }, + { title: 'Progress & analytics', href: '/docs/sdk/annotation-queues/analytics' }, + { title: 'Export', href: '/docs/sdk/annotation-queues/export' }, + { title: 'Data models', href: '/docs/sdk/annotation-queues/data-models' }, + ] + }, + ] + }, + { + group: 'Knowledge Base', + icon: 'brain', + items: [ + { title: 'Overview', href: '/docs/sdk/knowledgebase' }, + ] + }, + { + group: 'Protect', + icon: 'shield', + items: [ + { title: 'Overview', href: '/docs/sdk/protect' }, ] } ] diff --git a/src/pages/docs/evaluation/concepts/connectors.mdx b/src/pages/docs/evaluation/concepts/connectors.mdx deleted file mode 100644 index 37e42d84..00000000 --- a/src/pages/docs/evaluation/concepts/connectors.mdx +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: "Connectors" -description: "External tools an Agent Evaluator can call while it judges, from a built-in URL checker to your own MCP server" ---- - -## What a connector is - -A connector is an external tool you attach to an eval so the evaluator can use it while it judges. On its own, a judge reasons only over the text you hand it. With a connector, an [Agent Evaluator](/docs/evaluation/concepts/eval-types) can act mid-judgment: when the verdict needs a fact the response does not carry, it calls the connector, reads what comes back, and only then decides. - - AE["Agent Evaluator"] - AE -->|"needs an outside fact"| CN["Connector
URL checker or MCP tool"] - CN -->|"result"| AE - AE --> V["Verdict + reason"] -`} /> - -That loop is the difference: a plain judge scores what it can read, while a connected Agent Evaluator can go and check. - -## What you can connect - -- **Built-in tools**, like the **URL checker** that confirms a cited link actually resolves -- **Custom MCP connectors**: any tool you expose over the Model Context Protocol, so the evaluator calls your own service the same way it calls a built-in one - -## When a connector helps - -Connectors are used by **Agent Evaluators**, the eval type that can run multiple turns and use tools. Reach for one when the judgment needs a fact the response does not carry, like confirming a URL is real or checking a value against your own data. A single-pass LLM-as-Judge or a deterministic Code Eval does not use connectors. - -## Keep exploring - - - - Agent Evaluators are the type that can use tools - - - The library of evaluators you attach connectors to - - diff --git a/src/pages/docs/evaluation/guides/using-connectors.mdx b/src/pages/docs/evaluation/guides/using-connectors.mdx new file mode 100644 index 00000000..584ec58d --- /dev/null +++ b/src/pages/docs/evaluation/guides/using-connectors.mdx @@ -0,0 +1,54 @@ +--- +title: "Using Connectors" +description: "Give an Agent Evaluator external tools it can call while it judges" +--- + +A **connector** is an external tool you attach to an eval so the evaluator can use it while it judges. On its own, an [evaluator model](/docs/evaluation/concepts/evaluator-models) reasons only over the text you hand it. With tools, an [Agent Evaluator](/docs/evaluation/concepts/eval-types) can act mid-judgment: when the verdict needs a fact the response does not carry, it calls a tool, reads what comes back, and only then decides. + + AE["Agent Evaluator"] + AE -->|"needs an outside fact"| T["Tools
web search, KB lookup, your MCP connector"] + T -->|"result"| AE + AE --> V["Verdict + reason"] + style T fill:#2f2f2f,stroke:#ffffff,stroke-width:2px +`} /> + +## What the evaluator can call + +- **Built-in tools**, no setup beyond a toggle: web search when you turn on **Use Internet**, lookups in any knowledge base you attach, and walking the trace when the eval runs on one +- **Your connectors**: any tool you expose over the Model Context Protocol (MCP), which the evaluator calls the same way it calls a built-in + + +Only Agent Evaluators use tools. A single-pass LLM-as-Judge or a Code Eval scores what it's given, so the tool options don't appear for those types. + + +## When to reach for a connector + +Attach one when the verdict depends on something only your systems know: checking a claim against your own database, confirming an ID actually exists, verifying a rule that lives in an internal service. For public facts, the built-in web search usually covers it. + +## Attach a connector to an eval + + + + Connectors are registered once for your organization and shared across evals (and with Falcon AI). If your MCP server isn't registered yet, follow [MCP Connectors](/docs/falcon-ai/features/mcp-connectors) to add it. + + + Create or open an Agent Evaluator eval, and in the model bar under the instructions, open the **+** menu and choose **Connectors**. Select the connectors this eval should be able to call. The same menu holds the **Use Internet** toggle and knowledge bases. + + + Save the eval and run it anywhere evals run. The evaluator decides on each run whether a tool call is worth making, and the result comes back as a verdict and reason like any other eval. + + + +## Dive deeper + + + + Bring your own model as the evaluator + + + Turn eval scores into a merge gate + + From d7cd898fbc3cf703332393c605777e3dc839dc69 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Fri, 10 Jul 2026 18:37:52 +0530 Subject: [PATCH 41/42] docs(evaluation): address re-review on Error localization (evaluator model rename, drop false skip condition) Signed-off-by: khushalsonawat --- src/pages/docs/evaluation/concepts/error-localization.mdx | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/pages/docs/evaluation/concepts/error-localization.mdx b/src/pages/docs/evaluation/concepts/error-localization.mdx index 9e8b95d4..a220ccbb 100644 --- a/src/pages/docs/evaluation/concepts/error-localization.mdx +++ b/src/pages/docs/evaluation/concepts/error-localization.mdx @@ -5,23 +5,22 @@ description: "Pinpoint which input field caused an evaluation to fail" ## Which input caused the failure -When an evaluation fails, **error localization** narrows down the cause by analyzing which input field led to the failure, using the [judge model](/docs/evaluation/concepts/judge-models)'s explanation and the [eval template](/docs/evaluation/concepts/eval-templates)'s criteria. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible, so you can see whether the problem is in your data, your retrieval, or your instructions. This works the same whether you're testing in the Playground or reviewing a live trace in [Observe](/docs/observe). You turn it on with the Error Localization checkbox when you configure an eval, on the template or a single run; see [Running Evaluations](/docs/evaluation/guides/running-evaluations) for the flow. +When an evaluation fails, **error localization** narrows down the cause by analyzing which input field led to the failure, using the [evaluator model](/docs/evaluation/concepts/evaluator-models)'s explanation and the [eval template](/docs/evaluation/concepts/eval-templates)'s criteria. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible, so you can see whether the problem is in your data, your retrieval, or your instructions. This works the same whether you're testing in the Playground or reviewing a live trace in [Observe](/docs/observe). You turn it on with the Error Localization checkbox when you configure an eval, on the template or a single run; see [Running Evaluations](/docs/evaluation/guides/running-evaluations) for the flow. ## How it works 1. **Evaluation fails**: error localization runs when a result comes back failed, pass/fail evals by their own verdict, scored evals on a low score. -2. **Analysis**: an error-localizing agent receives the input data, the eval criteria, the eval result, and the judge's explanation, then works out which input drove the failure. +2. **Analysis**: an error-localizing agent receives the input data, the eval criteria, the eval result, and the evaluator model's explanation, then works out which input drove the failure. 3. **Result**: you get back an `error_analysis` block describing the problem and a `selected_input_key` naming the input field identified as the issue. 4. **Review**: the flagged field shows up in the trace detail eval view in Observe, and in the Playground's run tests. ## When it runs -Error localization runs on a failed result, as long as the eval isn't a code-type or composite eval and the result can actually be scored. It's skipped when: +Error localization runs on a failed result, as long as the eval isn't a code-type or composite eval. It's skipped when: - Evaluation passed, nothing to localize - Code-type eval, not supported - Composite eval, not supported yet -- Missing or non-numeric result, nothing to score ## Where results appear From 10aa452b189f71828b5f1d2336d479292cf93330 Mon Sep 17 00:00:00 2001 From: khushalsonawat Date: Fri, 10 Jul 2026 19:03:58 +0530 Subject: [PATCH 42/42] docs(evaluation): ship revamped Overview and Concepts to production Ships the rewritten Evaluation Overview and the ten concept pages (Understanding Evaluation, Eval types, Eval templates & versions, Evaluator models, Output types, Error localization, Guardrails, Composite evals, Feedback, Ground truth). The old features pages and their nav section stay live; the how-to guides ship separately later. Adds redirects for the two renamed concept URLs and completes the judge model -> evaluator model rename in the FAQ and cookbook. Signed-off-by: khushalsonawat --- src/lib/navigation.ts | 40 +---- src/lib/redirects.ts | 12 +- .../docs/cookbook/decrease-hallucination.mdx | 2 +- .../evaluation/eval-correction-loop.mdx | 14 +- .../docs/dataset/features/experiments.mdx | 4 +- .../docs/dataset/features/run-prompt.mdx | 8 +- .../evaluation/concepts/composite-evals.mdx | 4 +- .../concepts/error-localization.mdx | 4 +- .../evaluation/concepts/eval-templates.mdx | 2 +- .../docs/evaluation/concepts/eval-types.mdx | 2 +- .../evaluation/concepts/evaluator-models.mdx | 4 +- .../docs/evaluation/concepts/feedback.mdx | 4 +- .../docs/evaluation/concepts/ground-truth.mdx | 2 +- .../docs/evaluation/concepts/output-types.mdx | 4 +- .../evaluation/{guides => features}/cicd.mdx | 10 +- .../{guides => features}/custom-models.mdx | 16 +- .../custom-evals.mdx => features/custom.mdx} | 79 ++++---- .../docs/evaluation/features/evaluate.mdx | 170 ++++++++++++++++++ .../evaluation/features/futureagi-models.mdx | 95 ++++++++++ .../evaluation/guides/running-evaluations.mdx | 54 ------ .../evaluation/guides/using-connectors.mdx | 54 ------ src/pages/docs/evaluation/index.mdx | 6 +- src/pages/docs/faq.mdx | 2 +- .../simulation/features/prompt-simulation.mdx | 2 +- 24 files changed, 367 insertions(+), 227 deletions(-) rename src/pages/docs/evaluation/{guides => features}/cicd.mdx (98%) rename src/pages/docs/evaluation/{guides => features}/custom-models.mdx (91%) rename src/pages/docs/evaluation/{guides/custom-evals.mdx => features/custom.mdx} (56%) create mode 100644 src/pages/docs/evaluation/features/evaluate.mdx create mode 100644 src/pages/docs/evaluation/features/futureagi-models.mdx delete mode 100644 src/pages/docs/evaluation/guides/running-evaluations.mdx delete mode 100644 src/pages/docs/evaluation/guides/using-connectors.mdx diff --git a/src/lib/navigation.ts b/src/lib/navigation.ts index fb71b188..6b8ab2e3 100644 --- a/src/lib/navigation.ts +++ b/src/lib/navigation.ts @@ -322,40 +322,14 @@ export const tabNavigation: NavTab[] = [ ] }, { - title: 'Guides', - items: [ - { title: 'Running Evaluations', href: '/docs/evaluation/guides/running-evaluations' }, - { - title: 'Explore playground', - items: [ - { title: 'Overview', href: '/docs/evaluation/guides/explore-playground' }, - { title: 'Test an eval', href: '/docs/evaluation/guides/explore-playground/test-an-eval' }, - { title: 'Usage & analytics', href: '/docs/evaluation/guides/explore-playground/usage-analytics' }, - { title: 'Eval logs', href: '/docs/evaluation/guides/explore-playground/eval-logs' }, - ] - }, - { title: 'Create a custom eval', href: '/docs/evaluation/guides/custom-evals' }, - { title: 'Build a composite evals', href: '/docs/evaluation/guides/composite-evals' }, - { title: 'Set up guardrails', href: '/docs/evaluation/guides/guardrails' }, - { title: 'Add ground truth', href: '/docs/evaluation/guides/ground-truth' }, - { title: 'Collect feedback', href: '/docs/evaluation/guides/feedback' }, - { title: 'Using Connectors', href: '/docs/evaluation/guides/using-connectors' }, - { title: 'Use custom models', href: '/docs/evaluation/guides/custom-models' }, - { title: 'Evaluate in CI/CD', href: '/docs/evaluation/guides/cicd' }, - ] - }, - { - title: 'References', - items: [ - { title: 'Built-in evals', href: '/docs/evaluation/builtin' }, - { title: 'Output types & scoring', href: '/docs/evaluation/reference/output-types' }, - { title: 'SDK & API', href: '/docs/evaluation/reference/sdk-api' }, - ] - }, - { - title: 'Troubleshooting', + title: 'Features', items: [ - { title: 'Evaluation FAQ & fixes', href: '/docs/evaluation/troubleshooting' }, + { title: 'Built-in Evals', href: '/docs/evaluation/builtin' }, + { title: 'Evaluate via Platform & SDK', href: '/docs/evaluation/features/evaluate' }, + { title: 'Create Custom Evals', href: '/docs/evaluation/features/custom' }, + { title: 'Use Custom Models', href: '/docs/evaluation/features/custom-models' }, + { title: 'Future AGI Models', href: '/docs/evaluation/features/futureagi-models' }, + { title: 'Evaluate CI/CD Pipeline', href: '/docs/evaluation/features/cicd' }, ] }, ] diff --git a/src/lib/redirects.ts b/src/lib/redirects.ts index 6980ab33..a98e71c8 100644 --- a/src/lib/redirects.ts +++ b/src/lib/redirects.ts @@ -29,6 +29,8 @@ export const redirectMap: Record = { '/docs/tracing/auto-overview': '/docs/tracing/auto', '/docs/evaluation/builtin/eval-context-retrieval': '/docs/evaluation/builtin', '/docs/evaluation/features/groups': '/docs/evaluation', + '/docs/evaluation/concepts/judge-models': '/docs/evaluation/concepts/evaluator-models', + '/docs/evaluation/concepts/eval-results': '/docs/evaluation/concepts/output-types', '/docs/optimization/optimizers/overview': '/docs/optimization', '/docs/dataset/add-annotations': '/docs/dataset/features/annotate', '/docs/knowledge-base/concept': '/docs/knowledge-base/concepts/concept', @@ -173,12 +175,12 @@ export const redirectMap: Record = { '/future-agi/get-started/evaluation/builtin-evals/toxicity': '/docs/evaluation/builtin/toxicity', '/future-agi/get-started/evaluation/builtin-evals/translation-accuracy': '/docs/evaluation/builtin/translation-accuracy', '/future-agi/get-started/evaluation/builtin-evals/valid-links': '/docs/evaluation/builtin', - '/future-agi/get-started/evaluation/create-custom-evals': '/docs/evaluation/guides/custom-evals', - '/future-agi/get-started/evaluation/evaluate-ci-cd-pipeline': '/docs/evaluation/guides/cicd', - '/future-agi/get-started/evaluation/evaluate-patterns': '/docs/evaluation/guides/running-evaluations', + '/future-agi/get-started/evaluation/create-custom-evals': '/docs/evaluation/features/custom', + '/future-agi/get-started/evaluation/evaluate-ci-cd-pipeline': '/docs/evaluation/features/cicd', + '/future-agi/get-started/evaluation/evaluate-patterns': '/docs/evaluation/features/evaluate', '/future-agi/get-started/evaluation/future-agi-models': '/docs/evaluation/concepts/evaluator-models', - '/future-agi/get-started/evaluation/running-your-first-eval': '/docs/evaluation/guides/running-evaluations', - '/future-agi/get-started/evaluation/use-custom-models': '/docs/evaluation/guides/custom-models', + '/future-agi/get-started/evaluation/running-your-first-eval': '/docs/evaluation/features/evaluate', + '/future-agi/get-started/evaluation/use-custom-models': '/docs/evaluation/features/custom-models', '/future-agi/get-started/knowledge-base/concept': '/docs/knowledge-base/concepts/concept', '/future-agi/get-started/knowledge-base/how-to/create-kb-using-sdk': '/docs/knowledge-base/features/sdk', '/future-agi/get-started/knowledge-base/how-to/create-kb-using-ui': '/docs/knowledge-base/features/ui', diff --git a/src/pages/docs/cookbook/decrease-hallucination.mdx b/src/pages/docs/cookbook/decrease-hallucination.mdx index 3727b475..d4677500 100644 --- a/src/pages/docs/cookbook/decrease-hallucination.mdx +++ b/src/pages/docs/cookbook/decrease-hallucination.mdx @@ -592,7 +592,7 @@ The winner configuration was CharacterTextSplitter_mmr_map_rerank, which combine - **Can I create custom evaluations tailored to my RAG use case in Future AGI?** - Yes. The Deterministic Eval template in Future AGI supports custom evaluations (***Click [here](/docs/evaluation/guides/custom-evals) to learn more about deterministic eval***). This lets you apply stringent criteria to your RAG outputs minimising variability. + Yes. The Deterministic Eval template in Future AGI supports custom evaluations (***Click [here](/docs/evaluation/features/custom) to learn more about deterministic eval***). This lets you apply stringent criteria to your RAG outputs minimising variability. ## Ready to Reduce Hallucinations in Your RAG Applications? diff --git a/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx b/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx index 96e679cf..5d6a05c0 100644 --- a/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx +++ b/src/pages/docs/cookbook/evaluation/eval-correction-loop.mdx @@ -42,7 +42,7 @@ The example below uses SaaS customer-support replies. The trick: pick failure mo -Start with a built-in template like `is_helpful` or `tone`. It gives you a baseline plus the explanations the judge model used. The explanations are what you'll inspect in step 2. +Start with a built-in template like `is_helpful` or `tone`. It gives you a baseline plus the explanations the evaluator model used. The explanations are what you'll inspect in step 2. ```python import os @@ -101,7 +101,7 @@ for row in baseline_results: print(f"{row['id']}: eval={row['eval_score']!s:>5} | human={row['human_verdict']:>4} | {row['eval_reason'][:80]}") ``` -The built-in `is_helpful` eval will likely return `Passed` for `r2` and `r3`. Both replies are on-topic, well-formed, and offer a concrete action. Nothing about the surface form gives the generic judge a reason to fail them. Your team flags them as bad because they violate domain rules the judge has no way to know about. That's the disagreement signal the correction loop will fix. +The built-in `is_helpful` eval will likely return `Passed` for `r2` and `r3`. Both replies are on-topic, well-formed, and offer a concrete action. Nothing about the surface form gives the generic evaluator model a reason to fail them. Your team flags them as bad because they violate domain rules the evaluator model has no way to know about. That's the disagreement signal the correction loop will fix. @@ -128,7 +128,7 @@ Pick 2 or 3 disagreement rows that capture distinct failure modes (here: cheerfu -Create a custom eval whose rule prompt spells out your domain's definition of "good" and includes the corrected examples inline. The judge model uses the examples to calibrate its decisions on new rows. +Create a custom eval whose rule prompt spells out your domain's definition of "good" and includes the corrected examples inline. The evaluator model uses the examples to calibrate its decisions on new rows. ```python import requests @@ -180,7 +180,7 @@ print(resp.json()) # when you call `evaluator.evaluate(eval_templates=...)` in the next step. ``` -Two things make this work. First, the rule prompt enumerates the domain rules explicitly, so the judge model has criteria instead of vibes. Second, the few-shot examples cover the exact failure modes you found in step 2, so the judge sees what "FAIL" looks like for *your* domain. +Two things make this work. First, the rule prompt enumerates the domain rules explicitly, so the evaluator model has criteria instead of vibes. Second, the few-shot examples cover the exact failure modes you found in step 2, so the evaluator model sees what "FAIL" looks like for *your* domain. Version your eval names (`_v1`, `_v2`). Each iteration creates a new template so historical eval runs stay reproducible. You can compare v1 vs v2 head-to-head later. @@ -250,11 +250,11 @@ You ran a built-in eval, found rows where it disagreed with human judgment, enco ## Explore further - + Full reference for the custom eval template API - - Pick the right judge model: turing_small, turing_flash, turing_large + + Pick the right evaluator model: turing_small, turing_flash, turing_large Built-in vs custom templates and required-key conventions diff --git a/src/pages/docs/dataset/features/experiments.mdx b/src/pages/docs/dataset/features/experiments.mdx index cd91bea7..5b026a59 100644 --- a/src/pages/docs/dataset/features/experiments.mdx +++ b/src/pages/docs/dataset/features/experiments.mdx @@ -77,7 +77,7 @@ Experiment creation is a guided three-step flow: **Basic Info → Configuration Models you've added through Custom Models show up in the model picker for prompt configurations across all experiment types. - See [Custom Models](/docs/evaluation/guides/custom-models) for how to register a custom or self-hosted model. + See [Custom Models](/docs/evaluation/features/custom-models) for how to register a custom or self-hosted model. @@ -90,7 +90,7 @@ Experiment creation is a guided three-step flow: **Basic Info → Configuration **Compare against baseline (optional)**: pick a column from the dataset to compare model outputs against (typically a ground-truth or existing run-prompt column). Skip it if you don't have a reference output yet; you can still run the experiment, attach evals that don't need a baseline, and add a base column later by editing the experiment. - **Add evaluations**: click **Add Evaluation** and pick from the [built-in eval](/docs/evaluation/builtin) catalog or [create a custom eval](/docs/evaluation/guides/custom-evals). Add as many as you need. Every eval runs on every configuration so the results are directly comparable. + **Add evaluations**: click **Add Evaluation** and pick from the [built-in eval](/docs/evaluation/builtin) catalog or [create a custom eval](/docs/evaluation/features/custom). Add as many as you need. Every eval runs on every configuration so the results are directly comparable. ![Choosing Evals](/screenshot/product/dataset/how-to/experiments-in-dataset/7.png) For each eval, map its inputs (e.g. `output`, `input`, `expected`) to the model output or to dataset columns. Mapping is required before the experiment can run. diff --git a/src/pages/docs/dataset/features/run-prompt.mdx b/src/pages/docs/dataset/features/run-prompt.mdx index 7583abf3..7a09a37e 100644 --- a/src/pages/docs/dataset/features/run-prompt.mdx +++ b/src/pages/docs/dataset/features/run-prompt.mdx @@ -36,28 +36,28 @@ Run Prompt lets you add a new column to your dataset that is filled by a model ( Choose **LLM** for text generation (chat). Use for Q&A, summarization, or any text-in, text-out task. Select a chat model from the list; ensure the provider has an API key configured. ![LLM](/screenshot/product/dataset/how-to/run-prompt-in-dataset/3.png) - Click [here](/docs/evaluation/guides/custom-models) to learn how to create custom models. + Click [here](/docs/evaluation/features/custom-models) to learn how to create custom models. Choose **Text-to-Speech** to generate audio from text. The prompt output column will store audio (e.g. URLs). You can configure voice and format for supported TTS models. ![Text-to-Speech](/screenshot/product/dataset/how-to/run-prompt-in-dataset/4.png) - Click [here](/docs/evaluation/guides/custom-models) to learn how to create custom models. + Click [here](/docs/evaluation/features/custom-models) to learn how to create custom models. Choose **Speech-to-Text** to transcribe audio into text. Use when a column contains audio; the model output will be text in the new column. ![Speech-to-Text](/screenshot/product/dataset/how-to/run-prompt-in-dataset/5.png) - Click [here](/docs/evaluation/guides/custom-models) to learn how to create custom models. + Click [here](/docs/evaluation/features/custom-models) to learn how to create custom models. Choose **Image Generation** to create images from text (or image + text) prompts. The prompt output column will store image URLs. Select an image-generation model and ensure the provider has an API key configured. ![Image Generation](/screenshot/product/dataset/how-to/run-prompt-in-dataset/6.png) - Click [here](/docs/evaluation/guides/custom-models) to learn how to create custom models. + Click [here](/docs/evaluation/features/custom-models) to learn how to create custom models. diff --git a/src/pages/docs/evaluation/concepts/composite-evals.mdx b/src/pages/docs/evaluation/concepts/composite-evals.mdx index 0ec52136..efb534b8 100644 --- a/src/pages/docs/evaluation/concepts/composite-evals.mdx +++ b/src/pages/docs/evaluation/concepts/composite-evals.mdx @@ -97,10 +97,10 @@ Weights only change the weighted average function; the other four ignore them. P ## Keep exploring - + Run a composite on any surface - + Write a child template of your own diff --git a/src/pages/docs/evaluation/concepts/error-localization.mdx b/src/pages/docs/evaluation/concepts/error-localization.mdx index a220ccbb..97091e4e 100644 --- a/src/pages/docs/evaluation/concepts/error-localization.mdx +++ b/src/pages/docs/evaluation/concepts/error-localization.mdx @@ -5,7 +5,7 @@ description: "Pinpoint which input field caused an evaluation to fail" ## Which input caused the failure -When an evaluation fails, **error localization** narrows down the cause by analyzing which input field led to the failure, using the [evaluator model](/docs/evaluation/concepts/evaluator-models)'s explanation and the [eval template](/docs/evaluation/concepts/eval-templates)'s criteria. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible, so you can see whether the problem is in your data, your retrieval, or your instructions. This works the same whether you're testing in the Playground or reviewing a live trace in [Observe](/docs/observe). You turn it on with the Error Localization checkbox when you configure an eval, on the template or a single run; see [Running Evaluations](/docs/evaluation/guides/running-evaluations) for the flow. +When an evaluation fails, **error localization** narrows down the cause by analyzing which input field led to the failure, using the [evaluator model](/docs/evaluation/concepts/evaluator-models)'s explanation and the [eval template](/docs/evaluation/concepts/eval-templates)'s criteria. Instead of knowing only that an eval failed, you learn which input, whether the prompt, context, or query, was responsible, so you can see whether the problem is in your data, your retrieval, or your instructions. This works the same whether you're testing in the Playground or reviewing a live trace in [Observe](/docs/observe). You turn it on with the Error Localization checkbox when you configure an eval, on the template or a single run; see [Evaluate via Platform & SDK](/docs/evaluation/features/evaluate) for the flow. ## How it works @@ -32,7 +32,7 @@ Error localization runs on a failed result, as long as the eval isn't a code-typ ## Keep exploring - + Run an eval and see error localization results diff --git a/src/pages/docs/evaluation/concepts/eval-templates.mdx b/src/pages/docs/evaluation/concepts/eval-templates.mdx index 5cbb9a09..52d2e5d5 100644 --- a/src/pages/docs/evaluation/concepts/eval-templates.mdx +++ b/src/pages/docs/evaluation/concepts/eval-templates.mdx @@ -37,7 +37,7 @@ Each config runs at its module's level, and a config is what the platform loosel | **Covers** | 156 templates across 14 groups: quality, safety, factuality, RAG, bias, format, audio, image | Any domain-specific, business, or regulatory rule you define | | **Required inputs** | Defined per template (e.g. `input`, `output`, `context`) | You define the required keys in the template config | -[Built-in evals](/docs/evaluation/builtin) lists every template; [Create a custom eval](/docs/evaluation/guides/custom-evals) shows how to write your own. +[Built-in evals](/docs/evaluation/builtin) lists every template; [Create a custom eval](/docs/evaluation/features/custom) shows how to write your own. ## Required inputs and mapping diff --git a/src/pages/docs/evaluation/concepts/eval-types.mdx b/src/pages/docs/evaluation/concepts/eval-types.mdx index 8e414882..dec7d8df 100644 --- a/src/pages/docs/evaluation/concepts/eval-types.mdx +++ b/src/pages/docs/evaluation/concepts/eval-types.mdx @@ -60,7 +60,7 @@ flowchart TD 156 evaluators, each tagged with its type - + Write your own, usually as LLM-as-Judge diff --git a/src/pages/docs/evaluation/concepts/evaluator-models.mdx b/src/pages/docs/evaluation/concepts/evaluator-models.mdx index 35e69186..797e51f7 100644 --- a/src/pages/docs/evaluation/concepts/evaluator-models.mdx +++ b/src/pages/docs/evaluation/concepts/evaluator-models.mdx @@ -28,7 +28,7 @@ Future AGI ships proprietary models built for evaluation, not for general-purpos ## Bring your own LLM -You can also use your own model as the evaluator, from OpenAI, Bedrock, SageMaker, Vertex AI, Azure, or a custom endpoint. Reach for a custom model when you need a domain-tuned model, must keep inference in a region, or already pay for a model you want to reuse. See [Use custom models](/docs/evaluation/guides/custom-models). +You can also use your own model as the evaluator, from OpenAI, Bedrock, SageMaker, Vertex AI, Azure, or a custom endpoint. Reach for a custom model when you need a domain-tuned model, must keep inference in a region, or already pay for a model you want to reuse. See [Use Custom Models](/docs/evaluation/features/custom-models). ## Modality @@ -40,7 +40,7 @@ An evaluator model can only score inputs it can read. Image evals run on any of Where the Protect models do their real-time work - + Bring your own LLM as the evaluator diff --git a/src/pages/docs/evaluation/concepts/feedback.mdx b/src/pages/docs/evaluation/concepts/feedback.mdx index fe998b1e..3ce3c69a 100644 --- a/src/pages/docs/evaluation/concepts/feedback.mdx +++ b/src/pages/docs/evaluation/concepts/feedback.mdx @@ -43,7 +43,7 @@ The correction loop is the pattern that turns feedback into a better eval: run a 1. **Establish a baseline**: run a built-in eval on a batch of rows and compare its results to your manual verdicts. 2. **Find disagreements**: a disagreement is any row where the eval and your team reach different verdicts. These rows teach the evaluator something new. -3. **Encode corrections**: write a [custom eval](/docs/evaluation/guides/custom-evals) whose rule prompt states your domain rules and includes a few disagreement rows as few-shot examples for the evaluator model. +3. **Encode corrections**: write a [custom eval](/docs/evaluation/features/custom) whose rule prompt states your domain rules and includes a few disagreement rows as few-shot examples for the evaluator model. 4. **Re-score and measure**: run the new eval on the same batch and measure agreement against your verdicts. 5. **Iterate**: pull a fresh batch, add new examples, and increment the version. Most evals converge in a few rounds. @@ -65,7 +65,7 @@ Feedback can come from any surface where you evaluate, so you can close the loop ## Keep exploring - + Encode your corrections as rules the evaluator model follows diff --git a/src/pages/docs/evaluation/concepts/ground-truth.mdx b/src/pages/docs/evaluation/concepts/ground-truth.mdx index 6d5fd976..b2cca15d 100644 --- a/src/pages/docs/evaluation/concepts/ground-truth.mdx +++ b/src/pages/docs/evaluation/concepts/ground-truth.mdx @@ -75,7 +75,7 @@ The platform embeds ground truth asynchronously after you save a mapping, and re The reference-based templates, with the inputs each one expects - + Attach ground truth and run evals against it on any surface diff --git a/src/pages/docs/evaluation/concepts/output-types.mdx b/src/pages/docs/evaluation/concepts/output-types.mdx index b8543af6..791490de 100644 --- a/src/pages/docs/evaluation/concepts/output-types.mdx +++ b/src/pages/docs/evaluation/concepts/output-types.mdx @@ -60,7 +60,7 @@ These aggregates appear in the evaluation summary view and are tracked per eval ## Next steps -- [Evaluate via Platform & SDK](/docs/evaluation/guides/running-evaluations): Run an eval and see results. +- [Evaluate via Platform & SDK](/docs/evaluation/features/evaluate): Run an eval and see results. - [Eval templates](/docs/evaluation/concepts/eval-templates): How templates define what output type a result uses. - [Evaluator models](/docs/evaluation/concepts/evaluator-models): How the evaluator model produces the result and reason. -- [CI/CD pipeline](/docs/evaluation/guides/cicd): Track results by version across deploys. +- [Evaluate CI/CD Pipeline](/docs/evaluation/features/cicd): Track results by version across deploys. diff --git a/src/pages/docs/evaluation/guides/cicd.mdx b/src/pages/docs/evaluation/features/cicd.mdx similarity index 98% rename from src/pages/docs/evaluation/guides/cicd.mdx rename to src/pages/docs/evaluation/features/cicd.mdx index 3c6f14ce..456dd99d 100644 --- a/src/pages/docs/evaluation/guides/cicd.mdx +++ b/src/pages/docs/evaluation/features/cicd.mdx @@ -1,5 +1,5 @@ --- -title: "Evaluate in CI/CD" +title: "Evaluate via CI/CD Pipeline: Automated Quality Checks" description: "Run Future AGI evaluations in your CI/CD pipeline to assess model performance on every pull request and keep quality checks consistent before deployment." --- @@ -401,16 +401,16 @@ The workflow posts a comment on your PR with the current version identifier and ## Next Steps - + Run a single eval from the UI or SDK. - + Define eval templates to use in your pipeline. Run multiple evals together as a group. - - Bring your own LLM for evaluations + + Bring your own model for evaluations. diff --git a/src/pages/docs/evaluation/guides/custom-models.mdx b/src/pages/docs/evaluation/features/custom-models.mdx similarity index 91% rename from src/pages/docs/evaluation/guides/custom-models.mdx rename to src/pages/docs/evaluation/features/custom-models.mdx index b362c630..d9d8a4a5 100644 --- a/src/pages/docs/evaluation/guides/custom-models.mdx +++ b/src/pages/docs/evaluation/features/custom-models.mdx @@ -1,11 +1,11 @@ --- -title: "Use custom models" +title: "Use Custom Models for AI Evaluation in Future AGI" description: "Use your own or third-party models for evaluations in Future AGI via supported providers or a custom API endpoint with full configuration control." --- ## About -Evaluations need a model to act as the evaluator: to read each response and decide whether it passes, fails, or scores within a range. Custom models let you bring your own LLM as the evaluator instead of using Future AGI's built-in models. +Evaluations need a model to act as the judge: to read each response and decide whether it passes, fails, or scores within a range. Custom models let you bring your own judge instead of using Future AGI's built-in models. This matters when you have a model that knows your domain better, when you need inference to stay within a specific cloud provider or region, or when you want to track evaluation costs against a model you already pay for. @@ -17,13 +17,13 @@ Two ways to connect: - **Custom endpoint**: Connect any model behind an HTTP API, including self-hosted, fine-tuned, or proxy deployments. -Learn how to define eval rules that use your model: [Create custom evals](/docs/evaluation/guides/custom-evals). +Learn how to define eval rules that use your model: [Create custom evals](/docs/evaluation/features/custom). --- ## When to use -- **Control cost and compliance**: Bring your own LLM and set token costs so evaluation spend is tracked. Keep inference in your chosen region or provider for compliance. +- **Control cost and compliance**: Bring your own model and set token costs so evaluation spend is tracked. Keep inference in your chosen region or provider for compliance. - **Evaluate with a fine-tuned or internal model**: Run evals with a model tuned on your domain or hosted in-house by connecting it via the custom endpoint option. - **Unify evals across providers**: Add multiple models and use the same eval templates against each to compare quality or cost. - **Proxy or third-party APIs**: Connect any API-compatible endpoint when it is not one of the built-in providers. @@ -120,19 +120,19 @@ Fields you may see when adding a model (from a provider or custom). **Applies to ## Next Steps - + Run a single eval from the UI or SDK. - + Define eval rules and select your custom model. Run multiple evals together as a group. - + Built-in models available for evals. - + Run evals automatically in your pipeline. diff --git a/src/pages/docs/evaluation/guides/custom-evals.mdx b/src/pages/docs/evaluation/features/custom.mdx similarity index 56% rename from src/pages/docs/evaluation/guides/custom-evals.mdx rename to src/pages/docs/evaluation/features/custom.mdx index 7f04a6f5..66c648b6 100644 --- a/src/pages/docs/evaluation/guides/custom-evals.mdx +++ b/src/pages/docs/evaluation/features/custom.mdx @@ -1,39 +1,40 @@ --- -title: "Create a custom eval" -description: "Write your own eval when no built-in template fits" +title: "Create Custom Evaluation Templates in Future AGI" +description: "Define custom evaluation criteria and rules tailored to your use case, extending beyond the built-in templates available in Future AGI." --- -Every AI product has its own definition of a good response. When no [built-in template](/docs/evaluation/builtin) fits, a custom eval lets you encode that definition yourself: write the criteria once, in plain language, and Future AGI scores every response against it. +## About -Most custom evals are [LLM-as-Judge](/docs/evaluation/concepts/eval-types), where an [evaluator model](/docs/evaluation/concepts/evaluator-models) applies your rule in one pass and returns a reason with each verdict. Use a Code Eval when the rule is objective and computable, or an Agent Evaluator when one pass won't do. Once created, a custom eval works exactly like a built-in one: use it on a dataset, in a simulation, or from the SDK, by its name. +Every AI product has its own definition of a good response. Custom evals let you encode those rules and run them at scale, you write the criteria once, in plain language, and Future AGI scores every response against it automatically, returning a result and a reason for each one. + +Once created, a custom eval works exactly like any built-in template: use it on a dataset, in a simulation, or call it from the SDK. + +--- ## When to use -- Assess content against industry or regulatory standards that the default templates do not cover -- Enforce your organization's own guidelines for tone, format, or required disclosures -- Apply multi-criteria or weighted scoring through the rule prompt -- Validate a specific response structure, such as a JSON shape or a set of required fields +- **Domain-specific validation**: Assess content against industry or regulatory standards that aren’t in the default templates. +- **Business rule compliance**: Enforce your organization’s guidelines (tone, format, disclosures) in a repeatable eval. +- **Complex or weighted scoring**: Implement multi-criteria or custom scoring logic via your rule prompt. +- **Custom output formats**: Validate specific response structures or formats (e.g. JSON shape, required fields) with a tailored eval. + +--- ## How to -You can create a custom eval from the **UI** or via the **SDK** (by calling the REST API from your code). After the template is saved, run it from the UI or from the evaluation SDK using the template name. +You can create custom evals from the **UI** or via the **SDK** (by calling the REST API from your code). After the template is saved, run it from the UI or from the evaluation SDK using the template name. - Open your **dataset**, click **Evaluate** in the top-right, then **Add Evaluation**. Select **Create your own evals** to start the custom-eval flow. + Open your **dataset**, click **Evaluate** in the top-right, then **Add Evaluation**. Select **Create your own eval** to start the custom-eval flow. ![Open evaluation creation](/screenshot/product/evaluation/create-custom-evals/1.png) **Name**: unique name for the eval (lowercase letters, numbers, hyphens, and underscores only; cannot start or end with `-` or `_`). Used when you add the eval to a dataset or call it from the SDK. - **Model**: pick who applies your rule. This choice is also what decides the eval's type: - - - **Use Future AGI Agents**: a Future AGI model applies the rule (turing_large, turing_flash, turing_small, protect, protect_flash) - - **Use other LLMs**: a model you bring applies the rule; set one up in [Use custom models](/docs/evaluation/guides/custom-models) - - **Function based**: a Code Eval built on an existing function eval. The result is computed by code, so the rule prompt below doesn't apply - + **Model**: choose **Use Future AGI Models** (e.g. turing_large, turing_flash, turing_small, protect, protect_flash) or **Use other LLMs** (your own or external providers). For model details, see [Future AGI models](/docs/evaluation/features/futureagi-models) and [Use custom models](/docs/evaluation/features/custom-models). ![Configure basic settings](/screenshot/product/evaluation/create-custom-evals/2.png) @@ -41,19 +42,14 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the ![Define evaluation rules](/screenshot/product/evaluation/create-custom-evals/3.png) - Choose what the eval returns: - - - **Pass/Fail**: binary result, `1.0` pass and `0.0` fail - - **Percentage**: a score between 0 and 100 - - **Deterministic choices**: a category label; you define the set of allowed choices - + **Pass/Fail**: binary result (e.g. 1.0 pass, 0.0 fail). **Percentage (score)**: numeric score between 0 and 100. **Deterministic choices**: categorical result; provide a dict of allowed choices. ![Configure output type](/screenshot/product/evaluation/create-custom-evals/4.png) - - **Tags**: for filtering and organization - - **Description**: shown in the evaluation list - - **Check Internet**: allow the eval to fetch up-to-date information when needed - - **Required keys**: list the input variable names the eval expects (e.g. `input`, `output`, `user_query`, `chatbot_response`) + - **Tags**: for filtering and organization. + - **Description**: shown in the evaluation list. + - **Check Internet**: allow the eval to fetch up-to-date information when needed. + - **Required keys**: list the input variable names the eval expects (e.g. `input`, `output`, `user_query`, `chatbot_response`). ![Add optional settings and save](/screenshot/product/evaluation/create-custom-evals/5.png) @@ -61,7 +57,7 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the Click **Create Evaluation**. The new template appears in your evaluation list and can be added to any dataset or called via the SDK using the name you gave it. - In your dataset, click **Evaluate** then **Add Evaluation**, select the custom eval you created, map the **columns** to the rule-prompt variables, then click **Add & Run**. See [Running evaluations on Future AGI](/docs/evaluation/guides/running-evaluations) for the full run flow. + In your dataset, click **Evaluate** → **Add Evaluation**, select the custom eval you created, map the **columns** to the rule-prompt variables, then click **Add & Run**. See [Running your first eval](/docs/evaluation) for the full UI flow. @@ -97,7 +93,7 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the "tags": ["customer-service"], }, ) - print(response.json()) # {"status": true, "result": {"eval_template_id": "..."}} + print(response.json()) # {"eval_template_id": "..."} ``` @@ -127,16 +123,27 @@ You can create a custom eval from the **UI** or via the **SDK** (by calling the -## Dive deeper +--- + +## Next Steps - - - Run your custom eval on any surface + + + Run evals from the UI or SDK. + + + Add your custom eval to a group and run it with others. + + + Bring your own model for evaluations. + + + Built-in models available for evals. - - Bring your own LLM as the evaluator + + Run evals automatically in your pipeline. - - Run your custom eval on every pull request + + How evaluation fits into the platform. diff --git a/src/pages/docs/evaluation/features/evaluate.mdx b/src/pages/docs/evaluation/features/evaluate.mdx new file mode 100644 index 00000000..57a3ffe4 --- /dev/null +++ b/src/pages/docs/evaluation/features/evaluate.mdx @@ -0,0 +1,170 @@ +--- +title: "Evaluate via Platform and SDK in Future AGI" +description: "Run evaluations using the Future AGI platform UI or the Python SDK. Choose individual templates or batch runs for scalable model assessment." +--- + +## About + +Evaluation is how you measure whether your AI is actually doing what you want it to do. + +You give it an input (a prompt, a response, a conversation) and an eval scores it. The score tells you if the output was accurate, safe, on-topic, well-structured, or whatever quality you care about. Every evaluation returns a **result** (e.g. Passed / Failed, or a numeric score), and a **reason** explaining why. + +You can run evaluations two ways: + +- **Platform UI**: point-and-click on a dataset. No code required. +- **Python SDK**: call `evaluator.evaluate()` from your code, scripts, or CI pipeline. + +Both support the same [built-in eval templates](/docs/evaluation/builtin) (e.g. Toxicity, Groundedness, Tone) and any custom evals you've defined. + +--- + +## When to use + +- **Catch regressions before they ship**: Run evals in CI so a bad prompt change or model update gets flagged before it reaches production. +- **Score outputs at scale**: Attach evals to a dataset and every row gets a score automatically, without reviewing each one manually. +- **Enforce safety and compliance**: Check every response for toxicity, PII, bias, or data privacy issues as part of your standard pipeline. +- **Compare models or prompts**: Evaluate the same inputs across different models or prompt variations to see which performs better on your criteria. +- **Monitor quality over time**: Run the same evals repeatedly to track whether your AI's output quality is improving or degrading. + +--- + +## How to + +Choose **UI** or **SDK** below; each tab has the process in steps. + + + + + + You need a dataset to run evals from the UI. If you don’t have one, add a dataset first. See [Dataset overview](/docs/dataset). + ![Select a dataset](/screenshot/product/evaluation/evaluate/1.png) + + + Open your dataset, then click **Evaluate** in the top-right. The evaluation configuration panel opens. + ![Open the evaluation panel](/screenshot/product/evaluation/evaluate/2.png) + + + Click **Add Evaluation**. Choose a **built-in template** (e.g. Tone) or click **Create your own eval**. For a built-in template: click it, give it a **name**, and under **config** select the **dataset column(s)** to use as input (and output if required). + ![Add and run an eval](/screenshot/product/evaluation/evaluate/3.png) + ![Add and run an eval](/screenshot/product/evaluation/evaluate/4.png) + + + Optionally enable **Error Localization** to pinpoint which part of a row caused a failure. Select a **model** if the template requires one. Click **Add & Run** to score every row in the dataset. + ![Add and run an eval](/screenshot/product/evaluation/evaluate/5.png) + + + From the Add Evaluation flow, click **Create your own eval** to define a custom template (name, model, rule prompt, output type, and optional settings). After you save it, the new eval appears in the evaluation list and you can add it to your dataset as in the step above. For full details on creating and configuring custom evals, see [Create custom evals](/docs/evaluation/features/custom). + + + + + + Some evals can run without an API key using the standalone `evaluate()` function, including local metrics like `contains`, `faithfulness`, and LLM-as-judge. See the SDK reference for details. + + + + Install the package **ai-evaluation** and create an `Evaluator` with your Future AGI API key and secret. Prefer setting **FI_API_KEY** and **FI_SECRET_KEY** in the environment instead of passing them in code. See [Accessing API keys](/docs/admin-settings#accessing-api-keys). + + ```bash + pip install ai-evaluation + ``` + + ```python + from fi.evals import Evaluator + + evaluator = Evaluator( + fi_api_key="your_api_key", + fi_secret_key="your_secret_key", + ) + ``` + + + Call `evaluate` with the eval template **name** (e.g. `tone`), **inputs** (dict with the keys the template expects, e.g. `"input"`), and **model_name**. Many built-in (system) templates require a model. + + ```python + result = evaluator.evaluate( + eval_templates="tone", + inputs={ + "input": "Dear Sir, I hope this email finds you well. I look forward to any insights or advice you might have whenever you have a free moment" + }, + model_name="turing_flash", + ) + print(result.eval_results[0].output) + print(result.eval_results[0].reason) + ``` + + + For long-running or large runs, set `is_async=True`. The call returns immediately with an **eval_id**; the evaluation runs in the background. + + ```python + result = evaluator.evaluate( + eval_templates="tone", + inputs={"input": "Your text here"}, + model_name="turing_flash", + is_async=True, + ) + eval_id = result.eval_results[0].eval_id + ``` + + + Use `get_eval_result(eval_id)` to fetch the result when the evaluation has finished. The same method works for both sync and async runs (e.g. to re-fetch a result). + + ```python + result = evaluator.get_eval_result(eval_id) + print(result.eval_results[0].output) + print(result.eval_results[0].reason) + ``` + + + To use a template you created in the UI, pass its **name** as `eval_templates` and supply the **inputs** dict with the keys your template’s required_keys expect (e.g. `"input"`, `"output"`). Use the same template name you see in the evaluation list. + + ```python + result = evaluator.evaluate( + eval_templates="name-of-your-eval", + inputs={ + "input": "your_input_text", + "output": "your_output_text" + }, + model_name="model_name" + ) + + print(result.eval_results[0].output) + print(result.eval_results[0].reason) + ``` + + + + + For **system (built-in)** eval templates, **model_name** is required and must be one of the models listed for that template. The backend validates required input keys from the template’s config. + + + + + +For more eval templates and Future AGI models, see [Built-in evals](/docs/evaluation/builtin) and [Future AGI models](/docs/evaluation/features/futureagi-models). + + +--- + +## Next Steps + + + + Define your own eval rules and criteria. + + + Run multiple evals together as a group. + + + Bring your own model for evaluations. + + + Built-in models available for evals. + + + Run evals automatically in your pipeline. + + + How evaluation fits into the platform. + + diff --git a/src/pages/docs/evaluation/features/futureagi-models.mdx b/src/pages/docs/evaluation/features/futureagi-models.mdx new file mode 100644 index 00000000..7dce3c35 --- /dev/null +++ b/src/pages/docs/evaluation/features/futureagi-models.mdx @@ -0,0 +1,95 @@ +--- +title: "Use Future AGI Models for AI Evaluation and Scoring" +description: "Future AGI's proprietary judge models are trained on diverse datasets to perform accurate evaluations and score AI outputs." +--- + +## About + +When you run an evaluation, the model you choose determines how accurately and how fast each response gets scored. Future AGI provides a set of proprietary models built and optimized specifically for evaluation, not general-purpose chat or generation. + +Each model is designed for a different need. Some prioritize accuracy across complex multimodal inputs. Others are built for speed, making them suitable for real-time guardrailing or high-volume pipelines. Choosing the right model lets you balance quality and performance for your specific workload. + +All models are available in the platform UI and the SDK, and work with both built-in and custom eval templates. + +--- + +## Available models + +- **TURING_LARGE** `turing_large`: Flagship evaluation model that delivers best-in-class accuracy across multimodal inputs (text, images, audio). Recommended when maximal precision outweighs latency constraints. + +- **TURING_SMALL** `turing_small`: Compact variant that preserves high evaluation fidelity while lowering computational cost. Supports text and image evaluations. + +- **TURING_FLASH** `turing_flash`: Latency-optimised version of TURING, providing high-accuracy assessments for text and image inputs with fast response times. + +- **PROTECT** `protect`: Real-time guardrailing model for safety, policy compliance, and content-risk detection. Offers very low latency on text and audio streams and permits user-defined rule sets. + +- **PROTECT_FLASH** `protect_flash`: Ultra-fast binary guardrail for text content. Designed for first-pass filtering where millisecond-level turnaround is critical. + +--- +### Quick comparison + +| Model | Code | Inputs | Best for | Latency | +| --- | --- | --- | --- | --- | +| TURING_LARGE | `turing_large` | Text, image, audio | Max accuracy, multimodal evals | Higher | +| TURING_SMALL | `turing_small` | Text, image | High fidelity, lower cost | Medium | +| TURING_FLASH | `turing_flash` | Text, image | Fast, high-accuracy evals | Low | +| PROTECT | `protect` | Text, audio | Safety, guardrails, user-defined rules | Low | +| PROTECT_FLASH | `protect_flash` | Text | First-pass binary filtering | Ultra-low | + +--- + +## How to + + + + + + When adding or configuring an evaluation on a dataset or run test, choose **Use Future AGI Models** and pick a model from the dropdown. + ![Use Future AGI Models in the UI](/screenshot/product/evaluation/future-agi-models/1.png) + + + + + + + Pass `model_name` in your `evaluator.evaluate()` call. Use the model code from the table above (e.g. `turing_flash`, `turing_large`, `protect`). + + ```python + from fi.evals import Evaluator + + evaluator = Evaluator(fi_api_key="...", fi_secret_key="...") + result = evaluator.evaluate( + eval_templates="tone", + inputs={"input": "Your text to evaluate."}, + model_name="turing_small", # or turing_flash, turing_large, protect, protect_flash + ) + ``` + + + + + +--- + +## Next Steps + + + + Run evals from the UI or SDK. + + + Define your own eval rules and choose a model to run them. + + + Run multiple evals together as a group. + + + Bring your own model for evaluations. + + + Run evals automatically in your pipeline. + + + How evaluation fits into the platform. + + diff --git a/src/pages/docs/evaluation/guides/running-evaluations.mdx b/src/pages/docs/evaluation/guides/running-evaluations.mdx deleted file mode 100644 index b949adcf..00000000 --- a/src/pages/docs/evaluation/guides/running-evaluations.mdx +++ /dev/null @@ -1,54 +0,0 @@ ---- -title: "Running Evaluations" -description: "Where to run evals on Future AGI, and the guide for each surface" ---- - -Evaluations have no separate quickstart. An eval scores data, so you run it wherever your data already lives: a dataset, your live traces, a simulation, the playground, the SDK, or a CI job. The template you run is the same on every surface. The surface only changes what you feed it and where the score lands. - -This page is the entry point. Pick your surface below and follow its guide. - -## Where evals run - - D["Dataset"] - T --> TR["Traces"] - T --> S["Simulation"] - T --> P["Playground"] - T --> K["SDK"] - T --> C["CI/CD"] - D --> SC["Comparable scores"] - TR --> SC - S --> SC - P --> SC - K --> SC - C --> SC -`} /> - -In evaluation, **offline** and **online** describe the data, not your connection: an offline run scores data you've already stored (dataset rows, experiment outputs), an online run scores live production traffic as it arrives. - -| Surface | Where you run it | Guide | -|---|---|---| -| Dataset and experiments | Offline, over every row of a dataset | [Run experiments](/docs/dataset/features/experiments) | -| Traces | Online, on live spans, traces, and sessions | [Set up evals in Observe](/docs/observe/guides/setup-evals) | -| Simulation | Over simulated conversations | [Run a simulation](/docs/simulation/features/run-simulation) | -| Playground | On one sample input, to see the verdict and reason before you commit | [Explore playground](/docs/evaluation/guides/explore-playground) | -| SDK | Programmatic runs, with local Code Evals that need no API key | [Evaluation SDK](/docs/sdk/evals) | -| CI/CD | On every pull request, gating the merge on eval scores | [Evaluate in CI/CD](/docs/evaluation/guides/cicd) | - -The online how-to for scoring live traces lives in Observe; this hub only points to it. Everything else runs from its own module, with the same templates and the same scores. - -## Dive deeper - - - - Write your own when no built-in fits - - - 156 templates across 14 groups - - - Pick an evaluator model for your evals - - diff --git a/src/pages/docs/evaluation/guides/using-connectors.mdx b/src/pages/docs/evaluation/guides/using-connectors.mdx deleted file mode 100644 index 584ec58d..00000000 --- a/src/pages/docs/evaluation/guides/using-connectors.mdx +++ /dev/null @@ -1,54 +0,0 @@ ---- -title: "Using Connectors" -description: "Give an Agent Evaluator external tools it can call while it judges" ---- - -A **connector** is an external tool you attach to an eval so the evaluator can use it while it judges. On its own, an [evaluator model](/docs/evaluation/concepts/evaluator-models) reasons only over the text you hand it. With tools, an [Agent Evaluator](/docs/evaluation/concepts/eval-types) can act mid-judgment: when the verdict needs a fact the response does not carry, it calls a tool, reads what comes back, and only then decides. - - AE["Agent Evaluator"] - AE -->|"needs an outside fact"| T["Tools
web search, KB lookup, your MCP connector"] - T -->|"result"| AE - AE --> V["Verdict + reason"] - style T fill:#2f2f2f,stroke:#ffffff,stroke-width:2px -`} /> - -## What the evaluator can call - -- **Built-in tools**, no setup beyond a toggle: web search when you turn on **Use Internet**, lookups in any knowledge base you attach, and walking the trace when the eval runs on one -- **Your connectors**: any tool you expose over the Model Context Protocol (MCP), which the evaluator calls the same way it calls a built-in - - -Only Agent Evaluators use tools. A single-pass LLM-as-Judge or a Code Eval scores what it's given, so the tool options don't appear for those types. - - -## When to reach for a connector - -Attach one when the verdict depends on something only your systems know: checking a claim against your own database, confirming an ID actually exists, verifying a rule that lives in an internal service. For public facts, the built-in web search usually covers it. - -## Attach a connector to an eval - - - - Connectors are registered once for your organization and shared across evals (and with Falcon AI). If your MCP server isn't registered yet, follow [MCP Connectors](/docs/falcon-ai/features/mcp-connectors) to add it. - - - Create or open an Agent Evaluator eval, and in the model bar under the instructions, open the **+** menu and choose **Connectors**. Select the connectors this eval should be able to call. The same menu holds the **Use Internet** toggle and knowledge bases. - - - Save the eval and run it anywhere evals run. The evaluator decides on each run whether a tool call is worth making, and the result comes back as a verdict and reason like any other eval. - - - -## Dive deeper - - - - Bring your own model as the evaluator - - - Turn eval scores into a merge gate - - diff --git a/src/pages/docs/evaluation/index.mdx b/src/pages/docs/evaluation/index.mdx index 71875e71..1be48288 100644 --- a/src/pages/docs/evaluation/index.mdx +++ b/src/pages/docs/evaluation/index.mdx @@ -35,12 +35,12 @@ flowchart LR - [Observe](/docs/observe) records what your agent did, and its traces become eval inputs - Evaluate scores each response and shows the result back on the trace - [Optimization](/docs/optimization) consumes the scores to improve the prompt or model -- A threshold turns the score into a merge gate in [CI/CD](/docs/evaluation/guides/cicd) before anything ships +- A threshold turns the score into a merge gate in [CI/CD](/docs/evaluation/features/cicd) before anything ships ## Start here - + Every surface you can run an eval on, and how to get going @@ -82,7 +82,7 @@ Beyond the loop, the same evals run offline: [datasets](/docs/dataset) score row Attach an eval to a project's traces and score new data as it arrives - + Turn an eval threshold into a merge gate that blocks regressions diff --git a/src/pages/docs/faq.mdx b/src/pages/docs/faq.mdx index e1ccef7b..bb504976 100644 --- a/src/pages/docs/faq.mdx +++ b/src/pages/docs/faq.mdx @@ -33,7 +33,7 @@ Future AGI has 70+ built-in evaluation templates covering quality, safety, factu **How do I run my first evaluation?** -See [Evaluate via Platform & SDK](/docs/evaluation/guides/running-evaluations) for step-by-step instructions using the UI or Python SDK. +See [Evaluate via Platform & SDK](/docs/evaluation/features/evaluate) for step-by-step instructions using the UI or Python SDK. **How do I evaluate RAG applications?** diff --git a/src/pages/docs/simulation/features/prompt-simulation.mdx b/src/pages/docs/simulation/features/prompt-simulation.mdx index a602dd37..6a617ba0 100644 --- a/src/pages/docs/simulation/features/prompt-simulation.mdx +++ b/src/pages/docs/simulation/features/prompt-simulation.mdx @@ -159,7 +159,7 @@ Before you start: have a **prompt template** with at least one saved **prompt ve Use AI-powered suggestions to improve your prompt based on simulation results. - + Build evaluations tailored to your specific use case.