Definition: LLM evals are repeatable tests that measure how well a language model or AI application performs on the tasks it is meant to handle, using test cases, scoring methods and pass thresholds.
An eval set pairs realistic inputs with expected outputs or grading criteria. Scoring can use exact checks, code-based assertions, human review, or an LLM-as-judge that grades responses against a rubric. Public benchmarks such as MMLU compare general model capability, while application evals use your own data and your own definition of a good answer.
Changing a prompt, model, retrieval setting or temperature can quietly break behavior. Running evals on every change, often in CI/CD, catches regressions before users do and turns model choice into a measured decision.
Teams start with a few dozen cases drawn from real traffic, add every production failure as a new case, track scores over time, and pair offline evals with online monitoring.
Ryz LLM evaluation engineers build eval suites and quality gates in your repos, working on your team.
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