225 lines
14 KiB
Markdown
225 lines
14 KiB
Markdown
# Per-claim groundedness judge — bake-off **v3.1** (recall-hardened over v3's 3 confirmed FNs)
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v3.1 of `judge-claim-prompt-v3.md`. Same blind, per-claim, one-subagent-per-file
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design, same three verdicts, same output schema, same evidence discipline. v3.1
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changes only **three reasoning rules** (R1, R7, and a new R8), each fixing one of the
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**3 false negatives v3 still carried** (the judge-vs-gold disagreements G5 confirmed
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were genuine judge misses, not stale gold).
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**Why v3.1 exists — and what it is NOT (transparent, not p-hacking).** The G5b
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freshness spot-check (2026-06-30) blind-re-adjudicated v3's 4 apparent *false
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positives* against live Microsoft Learn. **All 4 turned out to be stale gold — v3
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flagged every one correctly** (`adr-template#1` "zero permission management" is
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contradicted; `multi-region#2` lists retired `gpt-35-turbo`; `network-resilience#4`
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overstates "recommended" as "obligatorisk"; `vector-storage#7` cites the wrong GA
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date). So **v3 has zero real false positives** (P = 100% on corrected gold), and the
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precision-side "FP-vakt" originally planned for v3.1 is **dropped — there is nothing to
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defend.** v3.1 is therefore a **pure recall hardening**: it tightens three rules so the
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judge catches 3 documented failure modes it currently misses, without touching the
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precision-side rules (R2, R5, R6 and v3's R7 capability-following are unchanged).
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**Adoption is gated on measurement, not assertion — and the bar is now v3 (P 100% /
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R 92.9% on corrected gold), not v2.** v3 sits at the precision ceiling, so v3.1 can only
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be adopted if it **holds P = 100% AND lifts R above 92.9%** (catches FNs without
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introducing a single new false positive). Any new FP drops P below 100% and fails the
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gate — keep v3. Recall rules are double-edged over the full population (v3's own
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bake-off taught this), so the 45-way fan-out, not this prose, decides. v3 results stay
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frozen; v3.1 writes to `judge-bakeoff-results-v3.1.json` and is graded against the
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corrected `gold-correctness-set.json`.
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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. **Exception: existence
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claims — see Rule R2.**
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## ⚠️ EXACT-VALUE RULE (inherited from v2 — still in force)
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A claim is `grounded` ONLY if the fetched page states the **exact** asserted value(s).
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Verifying that the page "is about" the SKU/model/feature is **not** enough — the
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specific number, name, date, tier, dimension, or status must match. If the claim
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asserts value **X** and the page states a **different** value **Y** (even if adjacent
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or plausible), the verdict is **`not_grounded`**. This rule does NOT lower the bar for
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`not_grounded`: you still need a fetched quote stating the **differing** value.
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Applies with special force to `sku`, `taxonomy`, `version`, `tpm`, `region`, `status`.
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---
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## CALIBRATION RULES — read all eight before judging
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The exact-value rule is a blunt instrument. The 8 rules below sharpen it on both
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edges: **R1–R4 and R8 catch real errors** (more `not_grounded`); **R5–R7 stop
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over-flagging where the core is grounded** (more correctly `grounded`). When a rule
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below conflicts with a literal reading of the exact-value rule, the rule below governs
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— it is the more precise standard.
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### Recall side — flag these as `not_grounded`
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**R1 — Bound understatement / overstatement (fixes FN2; v3.1 splits lower vs upper).**
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A claim may assert a **bound**. Direction matters — judge it by which side the bound
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constrains:
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- **Lower bound** ("100+", "200k+", "at least N", "minimum N"): do not auto-`grounded`
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it just because the true value satisfies the inequality. Apply the **lower-bound
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policy:** if the true current value **grossly exceeds** the stated bound — roughly
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**>2× and decision-changing** — the bound materially misleads → `not_grounded`. A
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*tight* lower bound (true value within the same order of magnitude) stays `grounded`.
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*Example (FN2): "200k+ context" while the page states 1,047,576 (~1M) — ~5× → `not_grounded`.*
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- **Upper bound** ("up to N", "opptil N", "maximum N", "no more than N", "as many as N"):
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this is a **ceiling**, not a floor. The lower-bound leniency does **NOT** apply. The
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exact-value rule governs: if the live page states a current maximum **higher** than N,
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the stated ceiling is **superseded** → `not_grounded` — **regardless of ratio** (even
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a 1.1× exceedance breaks a ceiling). The claim tells the reader the limit is N when it
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is really higher. *Example (v3-FN): claim "up to 18 underlying models" while the page
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states 28 → the ceiling has moved → `not_grounded`.* (Only `grounded` if the true
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maximum is N or the claim's ceiling still binds.)
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**R2 — `source_silent` does NOT excuse an existence claim (fixes FN3, FN5).** When the
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claim asserts that a named entity **exists / is offered / is in a list** ("X is a
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built-in judge", "feature Y is available", "tier Z exists"), and you fetch the
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authoritative page that *would* enumerate it and the entity is **absent**, that absence
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is **evidence the claim is wrong** — return `not_grounded`, not `source_silent`. Reserve
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`source_silent` for values a page would not be expected to enumerate (e.g. JS-rendered
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prices). State in `reason` that you checked the canonical enumerating page and the
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entity was not present. *Example (FN5): claim "99.99% SLA tier" while the reliability
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page lists only 99.9% → absence of any 99.99% tier = `not_grounded`.*
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**R3 — Frame/unit replacement (fixes FN4).** A claim's **organizing frame or unit** can
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be superseded even when derived ratios survive. If the page shows the claim's framing
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has been **replaced** (e.g. "1 Unit Capacity" → "Quota Tiers"; a renamed/retired
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metric), the claim is `not_grounded` even if some embedded numbers still appear
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somewhere — the claim describes a world that no longer exists. Check that the *unit and
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structure* the claim assumes still match the current page, not just the digits.
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**R4 — Current row, never a legacy row (fixes FN6).** Pages often carry historical or
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effective-dated rows ("Before April 3, 2024", "Legacy", "Retiring"). A claim is
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`grounded` only if it matches the **current/effective** row. Matching a clearly
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time-stamped *past* row is `not_grounded` (the value has since changed). Always locate
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the row that applies *today*. *Example (FN6): storage limits matching only the
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"Before April 3, 2024" row while current limits differ → `not_grounded`.*
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**R8 — Multi-part claims: every load-bearing part must hold (v3.1 — fixes the
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`ai-foundry-dr#9` FN).** A single claim often bundles **several load-bearing
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sub-assertions** (a status AND a region; a capability AND a named target; a date AND a
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GA level). Verify **each load-bearing part separately**. If **any one** load-bearing
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part is contradicted by the source, the whole claim is `not_grounded` — even when the
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other parts check out. Do not let a correct first half earn a `grounded` for a wrong
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second half. *Example (v3-FN): "Global training (Public Preview), cheaper, no data
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residency; use regional in Norway East" — the GA-vs-Preview part and the "no residency"
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part hold, but **Norway East is a Global (non-residency) training region, not a regional
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one** → one load-bearing part is wrong → `not_grounded`.* (R8 is the mirror of R6:
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R6 forgives an **omitted, non-load-bearing** detail; R8 condemns a **stated,
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load-bearing** part that is wrong. Decide first whether the part is load-bearing — if
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the claim *asserts* it and a reader would act on it, it is.)
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### Precision side — keep these `grounded` (do not over-flag)
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**R5 — Documented theoretical↔benchmark equivalence (fixes FP3).** Do not flag a
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numeric claim merely because the exact string is not verbatim, when the asserted value
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is the **documented theoretical or benchmark equivalent** of what the page states and
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both trace to Microsoft sources (e.g. a theoretical max vs a measured benchmark of the
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same technique, same order of magnitude, same direction). The exact-value rule targets
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*drifted/contradicting* values — not two Microsoft-sourced expressions of the same
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fact. If the page substantiates the magnitude and the technique, keep `grounded` and
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note the equivalence in `reason`.
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**R6 — Core grounded, detail omitted ≠ ungrounded (fixes FP4).** Distinguish "the
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claim's **core** assertion is grounded but it omits a sub-category" from "the core is
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ungrounded." If the page confirms the claim's **central** behavior/categorization and
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the only gap is an *unstated additional* case the claim did not deny, that is
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`grounded` (the claim is incomplete, not wrong). Reserve `not_grounded` for when the
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page **maps the core differently** or the claim **asserts** something the page
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contradicts. Omission ≠ contradiction. (Contrast R8: an omitted case is forgiven here;
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a *stated* but wrong load-bearing part is not — that is R8's domain.)
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**R7 — Follow the capability to its canonical page; don't punish illustrative numbers
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(fixes FP5; v3.1 sharpens the load-bearing carve-out).** If a claim asserts a **real
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capability** and the cited `evidence_url` does not foreground it, search for the
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**canonical** page that documents the capability before judging — do not return
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`not_grounded` merely because the *cited* page was a weak choice. And when a capability
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is solidly grounded, do **not** flag it over an *illustrative* attached number (e.g.
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"~0 RTO/RPO", "≈15 min") that the claim offers as an order-of-magnitude illustration
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rather than a cited spec. Judge the **capability**; treat an illustrative figure as
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grounded if the capability is.
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> **⚠️ Load-bearing carve-out (v3.1, fixes the `token-usage#3` FN).** R7's leniency
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> covers only *illustrative* values. It does **NOT** cover a value or **exact string**
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> that **IS the assertion** — a metric name, an API field, an SDK identifier, an enum
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> value, a specific date/version. When the claim's load-bearing content is the literal
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> name/string itself (e.g. "the metrics are `PromptTokens` and `CompletionTokens`"),
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> the exact-value rule applies in full: if the live page names them differently
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> (`ProcessedPromptTokens` / `InputTokens` / `GeneratedTokens` / `OutputTokens`), the
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> claim is `not_grounded`. "Follow to the canonical page" means find the **right
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> names**, not rescue wrong ones. A reader would copy that string into code; an
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> illustrative magnitude they would not.
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---
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## Procedure (per claim)
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1. **Identify the volatile assertion(s)** in the claim text — and when the claim
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bundles several (R8), enumerate **each load-bearing part**. 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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**Under R2/R7, actively seek the canonical enumerating/capability page** — a weak
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cited URL is not the last word.
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3. **Exact-value entailment check** each checkable value (and each load-bearing part
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under R8), then apply the calibration rules R1–R8. Classify which rule(s), if any,
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govern the claim. For R1, first decide whether the bound is a **lower** bound (floor)
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or an **upper** bound (ceiling) — they invert.
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4. **Strict evidence rule:** a `grounded` or `not_grounded` verdict REQUIRES a verbatim
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quote you actually fetched from a `learn.microsoft.com` URL. For R2 (existence
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absence), the quote is the canonical enumeration in which the entity does **not**
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appear — quote the enumeration and state the entity is absent. No quote → `source_silent`.
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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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- `rule` = which calibration rule governed, if any (`R1`–`R8`), else empty.
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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","rule":"<R1-R8 or empty>","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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