AI evals are systematic, repeatable measurements of an AI system's quality. Given a set of inputs, how well does the system produce acceptable outputs according to defined criteria? They matter disproportionately in AI engineering because LLM-based systems fail differently from traditional software.
There is no compiler error for a hallucination or stack trace for a subtly wrong answer. Model behavior also varies with prompt wording, model version, and retrieved context.
Without evals, teams rely on "vibe checks": trying a few inputs by hand and shipping when things look fine. That approach cannot detect regressions, cannot compare two prompts objectively, and collapses as the product grows.
With evals, every change (prompt edit, model swap, retrieval tweak) can be scored against a stable dataset before shipping, so iteration becomes fast and safe.
In practice, the quality of a team's eval suite is one of the strongest predictors of the quality of the product itself. Models are increasingly interchangeable; a well-curated eval set encoding what "good" means for your users is a durable, proprietary asset that competitors cannot copy.
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