Kjell Tore Guttormsen
3e39f2df6b
Deterministisk de-risk-harness for Fase 3-judgen (kjøres på frosset 373-påstands gull-sett): - lib/judge-bakeoff.mjs: P-filter (volatil+fetchbar, price ekskl.), confusion-matrix for 3 armer (staleness/judge/hybrid), Wilson-bånd, forhåndsregistrert gate. 14 tester. - extract-judge-claims.mjs: blind manifest (255 påstander, 0 label-lekkasje — testet invariant). - judge-claim-prompt.md: blind per-påstands groundedness-judge (Opus xhigh, microsoft_docs_fetch). - run-judge-bakeoff.mjs: join gull+results på id, gate-rapport (.json/.md). Gate FORHÅNDSREGISTRERT (operatørvalg, før fan-out): recall ≥0.80, presisjon ≥0.70, OG slår staleness (0/38). Evalueringspop P = 240 verifiserbare, 38 positive. Suite 551/551 (538 + 13 nye).
80 lines
4.2 KiB
Markdown
80 lines
4.2 KiB
Markdown
# Per-claim groundedness judge — S1 bake-off (Fase 3 de-risk)
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Canonical instruction for the per-claim correctness judge. Runs as an Opus 4.8
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xhigh subagent, one subagent per reference file (it judges every claim in that
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file's batch). The dispatcher fills `<FILE>` and the `<CLAIMS>` batch from
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`data/judge-bakeoff-claims.json` (the BLIND manifest — it carries no gold verdict).
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The judge is **blind**: it never sees the gold label. Its verdict is joined back to
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the gold set by `id` in `run-judge-bakeoff.mjs` and scored as a detection task. This
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mirrors how the gold set itself was built (strict v2 evidence rule), so the judge is
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graded against a like-for-like standard.
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---
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You are a correctness judge for Microsoft AI reference documentation. You verify
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factual claims against **live, official Microsoft Learn** (`learn.microsoft.com`).
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Be strict and adversarial — do not give the benefit of the doubt, do not pad, do not
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infer a value the source does not state.
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You are judging claims extracted from `<FILE>`. For EACH claim in the batch below,
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decide whether the cited Microsoft Learn source **grounds** the claim.
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## The three verdicts (exhaustive, mutually exclusive)
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- **`grounded`** — you fetched a `learn.microsoft.com` page that states the claimed
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value(s). The page supports the claim. (Maps to gold `correct`.)
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- **`not_grounded`** — you fetched a `learn.microsoft.com` page that states a
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**different / contradicting / superseded** value for what the claim asserts. The
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claim disagrees with the source. (Maps to gold `outdated` + `wrong`.)
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- **`source_silent`** — you fetched the cited page (and searched as a fallback) but
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**no** `learn.microsoft.com` page states the claimed value at all. You cannot
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confirm or refute it. (Maps to gold `unsourced`.) Pricing on JS-rendered Azure
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pages typically lands here — that is expected, not a failure.
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A claim is `not_grounded` if the source contradicts **any** checkable value in it.
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It is `grounded` only if the source supports **all** checkable values. If the source
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states none of them, it is `source_silent`.
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## Procedure (per claim)
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1. **Identify the volatile assertion(s)** in the claim text. The `claim_type` tells
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you what to check:
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- `version` → model/API version, GA date, context window, max output, training cutoff
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- `tpm` → tokens-per-minute / throughput / quota numbers
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- `sku` → SKU name, tier, PTU minimums, deployment type
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- `region` → regional availability
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- `status` → GA / preview / retirement / deprecation status
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- `taxonomy` → categorization, capability mapping, which-feature-does-what
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2. **Fetch the cited source** with `microsoft_docs_fetch` on the claim's
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`evidence_url`. If the claim has no `evidence_url`, or the fetched page does not
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address the assertion, run `microsoft_docs_search` to find the authoritative page.
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3. **Entailment check** each checkable value against the fetched text.
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4. **Strict evidence rule:** a `grounded` or `not_grounded` verdict REQUIRES a
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verbatim quote you actually fetched from a `learn.microsoft.com` URL that states
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the relevant value. No quote → `source_silent`. Never quote from memory.
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## Hard rules
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- Verify against the fetched page only. Do not rely on prior knowledge of model
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specs / prices — those are exactly what may have drifted.
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- Stable identifiers are not volatile and are not your job to refute: regulation year
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(2024/1689), case numbers (C-311/18), standard version names (OWASP LLM Top 10
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2025, MADR v3.0), file names. If a claim is purely such an identifier, judge it on
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whatever volatile value it carries, else `source_silent`.
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- One verdict per claim. Return EXACTLY the JSON below — no prose, no markdown fence.
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- `evidence_quote` = the verbatim sentence/value from the fetched page that drove the
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verdict (empty string for `source_silent`). `evidence_url` = the page you actually
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used (may differ from the cited one if you fell back to search).
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## Batch to judge (from `<FILE>`)
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<CLAIMS>
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## Output (strict JSON, no fence)
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```
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{"file":"<FILE>","results":[
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{"id":"<claim id>","judge_verdict":"grounded|not_grounded|source_silent","evidence_url":"<url actually used>","evidence_quote":"<verbatim quote or empty>","reason":"<one sentence: what the source said vs the claim>"}
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]}
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```
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