ms-ai-architect/scripts/kb-eval/judge-claim-prompt-v3.md

# Per-claim groundedness judge — bake-off **v3** (calibrated against 8 documented failure modes)

v3 of `judge-claim-prompt-v2.md`. Same blind, per-claim, one-subagent-per-file design,
same three verdicts, same output schema, same evidence discipline. v3 changes only the
**reasoning rules**, calibrated against the 8 judge-vs-gold disagreements adjudicated in
Spor 2b (`docs/ref-kb-gold-reconciliation-2026-06.md`).

**Why v3 exists (transparent, not p-hacking):** v2's exact-value rule correctly fixed
v1's *grounded-but-wrong* false negatives, but it **overshot** into false positives —
it flagged claims whose core was grounded over a pedantic exact-string miss (FP3), an
omitted sub-category (FP4), or an illustrative number on a solid capability (FP5). v2
also still **missed** a distinct class of false negatives: lower bounds that grossly
understate (FN2), `source_silent` masking real absence (FN3/FN5), a superseded
organizing frame whose derived numbers survive (FN4), and a match against a legacy
historical table row (FN6). v3 adds calibration on BOTH sides: tighter recall on the
4 missed FN modes, recovered precision on the 3 over-flagged FP modes. Each rule below
is a general correctness improvement, defensible independent of the test outcome.

**Adoption is gated on measurement, not assertion.** v3 must be graded against the
**reconciled** gold set and adopted ONLY if it beats v2 on P AND R (or holds one while
lifting the other) — adding nuance can swap precision for recall, so the bake-off, not
this prose, decides. v2 results stay frozen; v3 writes to
`judge-bakeoff-results-v3.json` and is graded against `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  **different / contradicting / superseded** value for what the claim asserts.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 v3 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`.

---

## v3 CALIBRATION RULES — read all seven before judging

The exact-value rule is a blunt instrument. The 7 rules below sharpen it on both
edges: **R1–R4 catch real errors v2 missed** (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` (v2 missed them)

**R1 — Lower-bound understatement (fixes FN2).** A claim may assert a *lower bound*
("100+", "200k+", "at least N", "up to 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 and the verdict is `not_grounded`.
A *tight* bound (true value within the same order of magnitude) stays `grounded`.
*Example (FN2): claim "200k+ context" while the model page states 1,047,576 (~1M) —
~5× understatement → `not_grounded`.*

**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`.*

### Precision side — keep these `grounded` (v2 over-flagged them)

**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.

**R7 — Follow the capability to its canonical page; don't punish illustrative numbers
(fixes FP5).** 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. (If the number is itself
the load-bearing assertion, the exact-value rule still applies.)

---

## Procedure (per claim)

1. **Identify the volatile assertion(s)** in the claim text. 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
2. **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.
3. **Exact-value entailment check** each checkable value, then apply the v3 calibration
   rules R1–R7. Classify which rule(s), if any, govern the claim.
4. **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 v3 calibration rule governed, if any (`R1`–`R7`), else empty.

## Batch to judge (from `<FILE>`)

<CLAIMS>

## Output (strict JSON, no fence)

```
{"file":"<FILE>","results":[
  {"id":"<claim id>","judge_verdict":"grounded|not_grounded|source_silent","rule":"<R1-R7 or empty>","evidence_url":"<url actually used>","evidence_quote":"<verbatim quote or empty>","reason":"<one sentence: what the source said vs the claim>"}
]}
```