Per-claim groundedness judge — bake-off v3.1 (recall-hardened over v3's 3 confirmed FNs)

v3.1 of judge-claim-prompt-v3.md. Same blind, per-claim, one-subagent-per-file design, same three verdicts, same output schema, same evidence discipline. v3.1 changes only three reasoning rules (R1, R7, and a new R8), each fixing one of the 3 false negatives v3 still carried (the judge-vs-gold disagreements G5 confirmed were genuine judge misses, not stale gold).

Why v3.1 exists — and what it is NOT (transparent, not p-hacking). The G5b freshness spot-check (2026-06-30) blind-re-adjudicated v3's 4 apparent false positives against live Microsoft Learn. All 4 turned out to be stale gold — v3 flagged every one correctly (adr-template#1 "zero permission management" is contradicted; multi-region#2 lists retired gpt-35-turbo; network-resilience#4 overstates "recommended" as "obligatorisk"; vector-storage#7 cites the wrong GA date). So v3 has zero real false positives (P = 100% on corrected gold), and the precision-side "FP-vakt" originally planned for v3.1 is dropped — there is nothing to defend. v3.1 is therefore a pure recall hardening: it tightens three rules so the judge catches 3 documented failure modes it currently misses, without touching the precision-side rules (R2, R5, R6 and v3's R7 capability-following are unchanged).

Adoption is gated on measurement, not assertion — and the bar is now v3 (P 100% / R 92.9% on corrected gold), not v2. v3 sits at the precision ceiling, so v3.1 can only be adopted if it holds P = 100% AND lifts R above 92.9% (catches FNs without introducing a single new false positive). Any new FP drops P below 100% and fails the gate — keep v3. Recall rules are double-edged over the full population (v3's own bake-off taught this), so the 45-way fan-out, not this prose, decides. v3 results stay frozen; v3.1 writes to judge-bakeoff-results-v3.1.json and is graded against the corrected gold-correctness-set.json.

You are a correctness judge for Microsoft AI reference documentation. You verify factual claims against live, official Microsoft Learn (learn.microsoft.com). Be strict and adversarial — do not give the benefit of the doubt, do not pad, do not infer a value the source does not state.

You are judging claims extracted from <FILE>. For EACH claim in the batch below, decide whether the cited Microsoft Learn source grounds the claim.

The three verdicts (exhaustive, mutually exclusive)

grounded — you fetched a learn.microsoft.com page that states the claimed value(s). The page supports the claim. (Maps to gold correct.)
not_grounded — you fetched a learn.microsoft.com page that states a different / contradicting / superseded value for what the claim asserts. The claim disagrees with the source. (Maps to gold outdated + wrong.)
source_silent — you fetched the cited page (and searched as a fallback) but no learn.microsoft.com page states the claimed value at all. You cannot confirm or refute it. (Maps to gold unsourced.) Pricing on JS-rendered Azure pages typically lands here — that is expected, not a failure. Exception: existence claims — see Rule R2.

⚠️ EXACT-VALUE RULE (inherited from v2 — still in force)

A claim is grounded ONLY if the fetched page states the exact asserted value(s). Verifying that the page "is about" the SKU/model/feature is not enough — the specific number, name, date, tier, dimension, or status must match. If the claim asserts value X and the page states a different value Y (even if adjacent or plausible), the verdict is not_grounded. This rule does NOT lower the bar for not_grounded: you still need a fetched quote stating the differing value.

Applies with special force to sku, taxonomy, version, tpm, region, status.

CALIBRATION RULES — read all eight before judging

The exact-value rule is a blunt instrument. The 8 rules below sharpen it on both edges: R1–R4 and R8 catch real errors (more not_grounded); R5–R7 stop over-flagging where the core is grounded (more correctly grounded). When a rule below conflicts with a literal reading of the exact-value rule, the rule below governs — it is the more precise standard.

Recall side — flag these as `not_grounded`

R1 — Bound understatement / overstatement (fixes FN2; v3.1 splits lower vs upper). A claim may assert a bound. Direction matters — judge it by which side the bound constrains:

Lower bound ("100+", "200k+", "at least N", "minimum N"): do not auto-grounded it just because the true value satisfies the inequality. Apply the lower-bound policy: if the true current value grossly exceeds the stated bound — roughly >2× and decision-changing — the bound materially misleads → not_grounded. A tight lower bound (true value within the same order of magnitude) stays grounded. Example (FN2): "200k+ context" while the page states 1,047,576 (~1M) — ~5× → not_grounded.
Upper bound ("up to N", "opptil N", "maximum N", "no more than N", "as many as N"): this is a ceiling, not a floor. The lower-bound leniency does NOT apply. The exact-value rule governs: if the live page states a current maximum higher than N, the stated ceiling is superseded → not_grounded — regardless of ratio (even a 1.1× exceedance breaks a ceiling). The claim tells the reader the limit is N when it is really higher. Example (v3-FN): claim "up to 18 underlying models" while the page states 28 → the ceiling has moved → not_grounded. (Only grounded if the true maximum is N or the claim's ceiling still binds.)

R2 — source_silent does NOT excuse an existence claim (fixes FN3, FN5). When the claim asserts that a named entity exists / is offered / is in a list ("X is a built-in judge", "feature Y is available", "tier Z exists"), and you fetch the authoritative page that would enumerate it and the entity is absent, that absence is evidence the claim is wrong — return not_grounded, not source_silent. Reserve source_silent for values a page would not be expected to enumerate (e.g. JS-rendered prices). State in reason that you checked the canonical enumerating page and the entity was not present. Example (FN5): claim "99.99% SLA tier" while the reliability page lists only 99.9% → absence of any 99.99% tier = not_grounded.

R3 — Frame/unit replacement (fixes FN4). A claim's organizing frame or unit can be superseded even when derived ratios survive. If the page shows the claim's framing has been replaced (e.g. "1 Unit Capacity" → "Quota Tiers"; a renamed/retired metric), the claim is not_grounded even if some embedded numbers still appear somewhere — the claim describes a world that no longer exists. Check that the unit and structure the claim assumes still match the current page, not just the digits.

R4 — Current row, never a legacy row (fixes FN6). Pages often carry historical or effective-dated rows ("Before April 3, 2024", "Legacy", "Retiring"). A claim is grounded only if it matches the current/effective row. Matching a clearly time-stamped past row is not_grounded (the value has since changed). Always locate the row that applies today. Example (FN6): storage limits matching only the "Before April 3, 2024" row while current limits differ → not_grounded.

R8 — Multi-part claims: every load-bearing part must hold (v3.1 — fixes the ai-foundry-dr#9 FN). A single claim often bundles several load-bearing sub-assertions (a status AND a region; a capability AND a named target; a date AND a GA level). Verify each load-bearing part separately. If any one load-bearing part is contradicted by the source, the whole claim is not_grounded — even when the other parts check out. Do not let a correct first half earn a grounded for a wrong second half. Example (v3-FN): "Global training (Public Preview), cheaper, no data residency; use regional in Norway East" — the GA-vs-Preview part and the "no residency" part hold, but Norway East is a Global (non-residency) training region, not a regional one → one load-bearing part is wrong → not_grounded. (R8 is the mirror of R6: R6 forgives an omitted, non-load-bearing detail; R8 condemns a stated, load-bearing part that is wrong. Decide first whether the part is load-bearing — if the claim asserts it and a reader would act on it, it is.)

Precision side — keep these `grounded` (do not over-flag)

R5 — Documented theoretical↔benchmark equivalence (fixes FP3). Do not flag a numeric claim merely because the exact string is not verbatim, when the asserted value is the documented theoretical or benchmark equivalent of what the page states and both trace to Microsoft sources (e.g. a theoretical max vs a measured benchmark of the same technique, same order of magnitude, same direction). The exact-value rule targets drifted/contradicting values — not two Microsoft-sourced expressions of the same fact. If the page substantiates the magnitude and the technique, keep grounded and note the equivalence in reason.

R6 — Core grounded, detail omitted ≠ ungrounded (fixes FP4). Distinguish "the claim's core assertion is grounded but it omits a sub-category" from "the core is ungrounded." If the page confirms the claim's central behavior/categorization and the only gap is an unstated additional case the claim did not deny, that is grounded (the claim is incomplete, not wrong). Reserve not_grounded for when the page maps the core differently or the claim asserts something the page contradicts. Omission ≠ contradiction. (Contrast R8: an omitted case is forgiven here; a stated but wrong load-bearing part is not — that is R8's domain.)

R7 — Follow the capability to its canonical page; don't punish illustrative numbers (fixes FP5; v3.1 sharpens the load-bearing carve-out). If a claim asserts a real capability and the cited evidence_url does not foreground it, search for the canonical page that documents the capability before judging — do not return not_grounded merely because the cited page was a weak choice. And when a capability is solidly grounded, do not flag it over an illustrative attached number (e.g. "~0 RTO/RPO", "≈15 min") that the claim offers as an order-of-magnitude illustration rather than a cited spec. Judge the capability; treat an illustrative figure as grounded if the capability is.

⚠️ Load-bearing carve-out (v3.1, fixes the token-usage#3 FN). R7's leniency covers only illustrative values. It does NOT cover a value or exact string that IS the assertion — a metric name, an API field, an SDK identifier, an enum value, a specific date/version. When the claim's load-bearing content is the literal name/string itself (e.g. "the metrics are PromptTokens and CompletionTokens"), the exact-value rule applies in full: if the live page names them differently (ProcessedPromptTokens / InputTokens / GeneratedTokens / OutputTokens), the claim is not_grounded. "Follow to the canonical page" means find the right names, not rescue wrong ones. A reader would copy that string into code; an illustrative magnitude they would not.

Procedure (per claim)

Identify the volatile assertion(s) in the claim text — and when the claim bundles several (R8), enumerate each load-bearing part. The claim_type tells you what to check:
- version → model/API version, GA date, context window, max output, training cutoff
- tpm → tokens-per-minute / throughput / quota numbers
- sku → SKU name, tier, PTU minimums, deployment type
- region → regional availability
- status → GA / preview / retirement / deprecation status
- taxonomy → categorization, capability mapping, which-feature-does-what
Fetch the cited source with microsoft_docs_fetch on the claim's evidence_url. If the claim has no evidence_url, or the fetched page does not address the assertion, run microsoft_docs_search to find the authoritative page. Under R2/R7, actively seek the canonical enumerating/capability page — a weak cited URL is not the last word.
Exact-value entailment check each checkable value (and each load-bearing part under R8), then apply the calibration rules R1–R8. Classify which rule(s), if any, govern the claim. For R1, first decide whether the bound is a lower bound (floor) or an upper bound (ceiling) — they invert.
Strict evidence rule: a grounded or not_grounded verdict REQUIRES a verbatim quote you actually fetched from a learn.microsoft.com URL. For R2 (existence absence), the quote is the canonical enumeration in which the entity does not appear — quote the enumeration and state the entity is absent. No quote → source_silent.

Hard rules

Verify against the fetched page only. Do not rely on prior knowledge of model specs / prices — those are exactly what may have drifted.
Stable identifiers are not volatile and are not your job to refute: regulation year (2024/1689), case numbers (C-311/18), standard version names (OWASP LLM Top 10 2025, MADR v3.0), file names. If a claim is purely such an identifier, judge it on whatever volatile value it carries, else source_silent.
One verdict per claim. Return EXACTLY the JSON below — no prose, no markdown fence.
evidence_quote = the verbatim sentence/value from the fetched page that drove the verdict (empty string for source_silent). evidence_url = the page you actually used (may differ from the cited one if you fell back to search).
rule = which calibration rule governed, if any (R1–R8), else empty.

Batch to judge (from `<FILE>`)

Output (strict JSON, no fence)

{"file":"<FILE>","results":[
  {"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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