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+# Evaluating the Summary Skill
+
+> Presentation deck — one slide per `---` separator.
+
+---
+
+## Evaluating the Summary Skill
+
+**Goal:** verify GoodData's dashboard-summary feature — both that it *works* and that it's *good*.
+
+**Challenge:** summaries are free text — non-deterministic, no single "correct" answer.
+
+**Strategy:** split into two concerns, test each with the right tool.
+
+---
+
+## Two concerns, two tools
+
+| | **Does it work?** | **Is it good?** |
+|---|---|---|
+| Concern | Integration / pipeline wiring | Content quality / regressions |
+| Tool | **Playwright e2e** (chat skill) | **`gd-eval`** (`/summary` endpoint) |
+| Asserts | A summary appears, no error | Rubric criteria, model A vs B |
+| Cadence | CI smoke | Eval / model-comparison runs |
+
+→ Don't force one tool to do both. Quality is `gd-eval`'s job; integration is Playwright's.
+
+---
+
+## The two server-side paths
+
+| | **`/summary` endpoint** | **Chat skill (`userContext`)** |
+|---|---|---|
+| Endpoint | `POST .../ai/workspaces/{ws}/summary` | `POST .../chat/.../messages` |
+| Input | viz IDs + dashboard ID + filters | full context **incl. `resultId`s** |
+| AFM exec | **server-side (automatic)** | must be done first (frontend does it) |
+| Response | plain JSON (sync) | SSE stream |
+
+- **Quality** → evaluate via the **endpoint** (clean JSON, no AFM/SSE plumbing).
+- **Chat skill** → smoke-test via **Playwright** (the real UI builds `userContext` + runs AFM for free).
+
+---
+
+## Quality eval — how `gd-eval` does it
+
+1. New `SummaryClient` POSTs the request → gets JSON `{ summary, ... }`.
+2. Summary text wrapped into the existing `ChatResult`.
+3. New `dashboard_summary` evaluator scores it with the **LLM judge**.
+4. Standard scores out: `pass@K`, `quality_score`, `value_score`, `latency`.
+5. Optional **Langfuse** experiment logging — unchanged.
+
+---
+
+## Why not exact-match? → Rubric scoring
+
+A good summary can be phrased many valid ways → **don't compare strings**.
+
+`expected_output` is a **rubric of checkable criteria**; the judge scores each true/false:
+
+```
+quality_score = (criteria satisfied) / (total criteria)
+```
+
+- Partial credit (7/8 facts = 0.875) → smooth, stable metric.
+- Non-determinism averages out instead of flipping pass/fail.
+- Plugs into existing scoring — no core change.
+
+---
+
+## What goes in the dataset
+
+```jsonc
+{
+  "id": "sales-overview-01",
+  "test_kind": "dashboard_summary",
+  "question": "Summarize the Sales Overview dashboard",   // label
+  "summary_input": {                       // → request to /summary
+    "dashboard_id": "sales_overview",
+    "visualizations": ["revenue_by_quarter", "revenue_by_region"],
+    "filter_context": [],
+    "format_hint": "3 bullet points"
+  },
+  "expected_output": {                     // → the rubric (judge target)
+    "must_include":     ["Revenue ≈ $4.2M", "Grew QoQ", "West is top region"],
+    "must_not_include": ["Segments/metrics not in the data", "Fabricated numbers"],
+    "rubric":           ["Captures trend", "Names best/worst segment", "Respects format"]
+  }
+}
+```
+
+- **must_include** → facts that define **pass/fail**
+- **must_not_include** → hallucination / accuracy guards
+- **rubric** → soft quality dimensions (graded only)
+
+---
+
+## Handling non-determinism
+
+- **Agent variance** → run `--runs K`; watch **mean quality + pass rate**, not just best-of-K.
+- **Judge variance** → low temperature + **decomposed yes/no criteria** (biggest lever).
+- **Ground facts in real data** — numbers must match what the visualizations return (allow tolerance, "≈ $4.2M").
+- ⚠️ Datasets are **workspace-coupled** (IDs + expected numbers tied to one environment).
+
+---
+
+## Chat skill — Playwright smoke test
+
+Tests the *real* pipeline: UI → chat → summary skill → AFM → LLM.
+
+- **The UI does the hard parts for free** — assembles `userContext`, executes AFM for `resultId`s. No Python AFM client needed.
+- **Keep assertions loose:** summary panel renders, text non-empty, no error, within timeout.
+- ❌ Do **not** assert content quality here — that's what makes e2e flaky.
+
+⚠️ **Prerequisite:** confirm the chat skill and the `/summary` endpoint share the **same summarizer**. If yes → endpoint quality-eval covers the skill's content too. If they diverge → chat-skill quality is only smoke-covered.
+
+---
+
+## Out of the box vs. to be implemented
+
+**✅ Reused as-is**
+- CLI, connection, profiles, model/provider selection
+- Run loop, `pass@K`, `quality`/`value`/`latency` scores
+- LLM-as-judge infrastructure (`[llm-judge]` extra)
+- Console + JSON reports; Langfuse source & sink
+
+**🛠️ To implement — quality (`gd-eval`)**
+- `SummaryClient` → calls `/summary`, maps JSON → `ChatResult`
+- `dashboard_summary` test kind + `summary_input` dataset fields
+- Multi-criterion judge (rubric → list of booleans)
+- Backend dispatch by `test_kind`; docs + tests
+
+**🛠️ To implement — integration (Playwright)**
+- One e2e smoke test in the web-app suite
+
+---
+
+## Summary
+
+- **Two concerns, two tools:** quality via `gd-eval` + `/summary`; integration via Playwright.
+- **Rubric-based dataset** — facts to include, things to avoid, quality criteria.
+- **Graded scoring** tames non-determinism; reuses existing `gd-eval` metrics.
+- **Playwright** smoke-tests the chat skill cheaply (UI builds the hard parts).
+- **First check:** do both paths share the summarizer? Drives whether quality is fully covered.