Evals - how AI quality gets measured
An "eval" is a test set used to measure whether an AI system is actually good at its job - a fixed set of example inputs with known correct (or graded) outputs, run automatically to score a model or prompt. Without evals, teams are just "vibes-checking" outputs, which doesn't scale and hides regressions.
Good evals matter more than picking the "best" model, because model quality varies wildly by task - a model great at coding might be mediocre at your specific classification task. Serious AI teams build a private eval set from real examples of their use case, then measure any prompt/model change against it before shipping, the same way software teams use test suites - tools like LangSmith, Braintrust, and OpenAI's own Evals framework exist specifically to run and track this.
Advisory case: if you're ever hiring someone to build an AI feature, one strong question is "how will we know if this is actually working well, beyond it looking fine in a demo?" - the answer should involve some form of eval set, not just "we tried it and it seemed good."
What is an "eval," in plain terms?