bcquality/evaluation/README.md
Jesper Schulz-Wedde f76279057e Generalize review fixture discovery
Derive smoke cases from the leaf, domain, and paired-sample conventions so new leaves require no scoring-contract changes. Keep only exceptional selection/context overrides and fail when retrieval metadata cannot rank the selected article.

Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>

Copilot-Session: 9825b012-e653-496a-9310-c1f4b6f8ac27
2026-07-15 09:11:15 +02:00

2.8 KiB

AL review evaluation

The evaluation is convention-driven. For every microsoft/skills/review/al-<domain>-review.md leaf, the harness finds microsoft/knowledge/<domain>/, selects the first article (by filename) with both .bad.al and .good.al companions, and derives the expected positive and clean control automatically. Adding a conforming leaf requires no scoring-contract edit.

review-fixtures.json contains only global thresholds and optional exceptional overrides. An override may select a different article or add context when the generic convention cannot express a scenario. It should remain empty in the normal case.

Model-facing preparation hashes case IDs, neutralizes Good/Bad object-name tokens, and removes full-line sample comments so neither the article slug, domain, nor expected outcome reveals the answer.

Validate the corpus

pwsh ./tools/Test-ReviewFixtures.ps1 -Root .

This credential-free check proves every registered leaf maps to a same-named knowledge domain with at least one complete AL sample pair and that all configured overrides are valid.

Run a fast-model evaluation

  1. Prepare neutral inputs:

    pwsh ./tools/Test-ReviewFixtures.ps1 -Root . -PrepareDirectory ./.evaluation-run
    

    This is also the CI path. It derives all cases, builds the current index, requires the convention-selected article to rank naturally into the candidate cutoff, and prepares the neutral requests.

  2. For a fast/small model, use one fresh invocation per request-case-*.json. Each request embeds the exact leaf instructions, that domain's candidate index rows with authoritative paths, and one opaque case. The model opens only matching articles and copies finding IDs from candidateArticles[].path. Save each response with the matching result-case-*.json name in the same directory.

    request-<domain>.json files provide optional two-case leaf batches; save those as result-<domain>.json. Directory scoring prefers result-case-*.json when present and otherwise falls back to result-*.json. review-request.json is an optional all-domains stress test for larger models. Neither batch form is the preferred fast-model profile.

  3. Save only this result shape:

    {
      "cases": [
        {
          "id": "case-a1b2c3d4",
          "findings": [
            { "id": "microsoft/knowledge/appsource/object-affixes-prevent-collisions.md" }
          ]
        }
      ]
    }
    

    Include every case. A clean control has an empty findings array.

  4. Score all per-leaf results together:

    pwsh ./tools/Test-ReviewFixtures.ps1 -Root . -ResultsDirectory ./.evaluation-run
    

    For a single combined stress-test result, use -ResultsPath instead.

The committed gate requires full expected recall, the exact convention-derived article ID, and no findings on clean controls.