ai-analyst-lab Evaluate a grader
Freeze the human labels before running the grader. Use one narrow criterion with a written rubric and structured output. The grader must be able to return unknown or request human review.
Keep human labels outside the judge workspace. Use python3 -m helpers.evals.cli run-isolated-judge to create a fresh child workspace containing only the named examples and current rubric. Run every revised rubric in a different fresh child. A child must not receive human labels, prior verdicts, captured verdicts, or later rubric versions. Preserve the isolation record with the verdicts.
Use helpers.evals.judges.evaluate_alignment for the confusion table and disagreement set. Repeat at least one unchanged boundary example and use repeated_label_stability to measure scoring stability.
Inspect:
- every human and grader disagreement;
- label imbalance;
- an answer-order reversal when judging pairs;
- a verbosity trap where a longer answer is not the better answer;
- an ambiguous example that should produce
unknown; and - whether the generator and grader are actually independent contexts.
Revise one rubric criterion at a time and rerun only the working examples. Do not tune on the heldout judge set.
Call the classroom result an alignment check. A small agreeing sample is not completed calibration.