What Are LLM Evals and How Do They Work?

These are formalized tests that evaluate how well a model or LLM application does its job. You define test cases and criteria, run the model, and grade the results, translating quality into metrics you can compare and track.

Because you will not know whether a change has been positive or negative without them. Evaluations help detect regressions before users see them, allow comparisons between models on evidence, and help track whether the system is improving.

Frequently with model-based scoring, where an additional model evaluates the output against certain criteria, or with human assessment for the most critical cases. Exact-match scoring applies only when there is one right answer.

Evals test quality by comparing against known cases, normally prior to release or whenever there is an update. Monitoring and observability watch the actions the system performs in production. Together they cover both pre-release testing and live behavior.

Large enough to represent real usage, including the significant cases and corner cases, not only the easy ones. A few carefully selected cases are more helpful than a large number that do not represent actual system usage.