You can't ship what you can't measure. A hands-on guide to building eval sets, scoring quality and catching regressions before your users do.
Why evals come first
AI systems fail quietly: a prompt tweak that helps one case silently breaks ten others. Without evaluations you're flying blind, and you'll hear about regressions from angry users instead of a dashboard.
Build a representative eval set
Curate real examples — including the edge cases and past failures — with known-good answers. A few hundred well-chosen cases beat thousands of generic ones.
Score what matters
Combine automatic metrics, model-graded scoring for nuanced quality, and human review on a sample. Track faithfulness, correctness and safety separately; a single blended number hides too much.
Make it a gate
Run evals in CI so nothing ships without passing, and keep scoring in production. Testing AI isn't a phase — it's the discipline that lets you iterate without fear.