Evals are structured tests that measure how well an AI model does the job you need it to do. You build a set of test cases, run the model against them, and get a score. That score is what tells you whether a new prompt, a new model or a new setting actually helped, rather than just felt different.
Think of it like a driving test rather than a vibe. You don’t pass someone because they “seem like a good driver”; you put them through set manoeuvres and mark each one. Evals do the same for AI: a fixed set of questions, consistent scoring, a clear pass or fail. This is closely related to broader model evaluation and to public benchmarks, the difference being that good evals test your specific use case, not a generic leaderboard.
The cases that earn their place are the awkward ones. A support bot eval should include the rude customer, the half-typed question, the request the bot must refuse. Those edge cases catch a hallucination or a confident wrong answer long before a real user runs into it, and they are usually where a model quietly gets worse after a change.
For open-ended answers, one common trick is to have a strong model grade the output, an approach known as LLM-as-judge. It scales far better than reading every answer by hand. The pitfall is a tiny eval set: ten happy-path examples will pass almost anything and tell you almost nothing, so coverage matters more than a high score.
At TopDevs we write evals for every AI feature we ship, so we can catch problems before a client’s customers do and keep quality steady as the system changes.
