Evals are the AI equivalent of unit tests: a curated set of inputs and the expected (or judged) outputs, run on every prompt or model change. Without evals, you have no idea whether your last "improvement" actually improved anything — or quietly regressed five other cases.
Three eval styles, in order of usefulness: (1) Exact match for structured tasks (classification, extraction). (2) Regex / contains for things you can detect mechanically. (3) LLM-as-judge when you need to grade quality, but always with a clear rubric and human spot-checks. Most teams skip evals and ship vibes; that's why their AI feature works in demos and breaks in production.
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