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Runtime AI evaluation (ADR 0004)

Two committed case sets and one harness, python -m permit_pathways.ai.eval. The scoring is model-independent; the model under test is whatever PERMIT_AI_PROVIDER / PERMIT_AI_MODEL select.

What is measured

Intake extraction (intake-cases.json, 40 synthetic cases, 25 English and 15 Spanish, across ADU, JADU, SB 9 two-unit, and SB 9 lot split, with deliberately underspecified and inference-tempting cases). Each case has a gold extraction over the material fields for its project type. Per field:

  • field_exact_match — predicted equals gold, where unknown == unknown counts as a match because abstaining was the right answer;
  • abstained_when_should — of the fields whose gold is unknown, the share the model returned as unknown;
  • filled_when_unknown — of the same fields, the share the model filled with a concrete value. This is the defect rate the design exists to hold down ("absence rendered as a value"); it is reported on its own, never folded into accuracy;
  • known_field_exact / known_field_missed / known_field_wrong — on fields the text does state, whether the model got it, abstained, or returned a different value;
  • project-type and jurisdiction-slug accuracy, and the share of cases in which every field is right.

Citation grounding (grounding-cases.json, 8 confirmed-fact intakes). Each intake is run through the deterministic matcher and then the explanation prompt. A claim is shown only if every quote it cites occurs verbatim in the named corpus document; otherwise it is withheld. Reported: claims generated, shown, withheld, and the fraction with verified citations. The same run drafts staff questions and reports what share carry a pointer that resolves to a matched rule or an unresolved fact.

What these numbers do not measure: legal fidelity, whether a shown claim is a correct reading of the passage it cites, Spanish quality, or comprehension. A verified citation proves the passage exists and says those words.

Results

results/ holds one JSON file per recorded run. Each records run.status (recorded_live_run or not_run), provider, model, prompt versions, UTC date, and the Git commit the run used, then the per-case detail. tests/test_ai_eval.py rejects a result file that claims a number without that provenance. Numbers are never written by hand.

Running

uv sync --extra ai
PERMIT_AI_PROVIDER=anthropic PYTHONPATH=src .venv/bin/python -m permit_pathways.ai.eval intake \
  --cases evals/ai/intake-cases.json --output evals/ai/results/<date>-intake-<provider>-<model>.json
PERMIT_AI_PROVIDER=bedrock PERMIT_AI_MODEL=global.anthropic.claude-sonnet-4-6 PYTHONPATH=src \
  .venv/bin/python -m permit_pathways.ai.eval grounding \
  --cases evals/ai/grounding-cases.json --output evals/ai/results/<date>-grounding-<provider>-<model>.json

--limit N runs the first N cases. Exit 1 means at least one case errored (the result file lists them); 2 means no provider could be configured.

Changing the cases

Gold follows the extraction policy in permit_pathways.ai.intake: ordinary-meaning readings are allowed ("backyard cottage" is a new detached ADU; "my house" is an existing single-family home), and a fact the text does not state stays unknown. Changing the policy changes the gold; record both in the same change. Every gold value must be in the vocabulary, and the gold field set must be exactly the material fields for the gold project type — the loader refuses anything else.