Evals are systematic tests of AI system quality: curated input sets, scoring methods (exact checks, statistical metrics, calibrated LLM judges), and pass criter
Evals are systematic tests of AI system quality: curated input sets, scoring methods (exact checks, statistical metrics, calibrated LLM judges), and pass criteria, run before releases, after model changes, and continuously in production. They are to AI what unit tests are to code.
Because AI systems fail by statistical drift that no manual spot-check would catch. Evals catch those regressions automatically before they reach users, which is why eval engineering became one of the most in-demand AI skills.