
Eval Reports That Drive AI Iterations
Author outlines a 'physical exam' style for AI model eval reports: conclusion-first, reproducible snapshots, actionable scores, key metrics, and typical cases to enable fast decisions on launch or fixes. Treats benchmarks as assets with anti-leakage maintenance and badcase regression. Turns evals into team systems for quicker iterations.








