Why evals matter
Shipping LLM features on vibes is how teams regress in production without noticing. A prompt tweak that “feels better” on three examples can quietly break a dozen edge cases. Evals replace that guesswork with measurement: a repeatable suite of scored tests over a representative dataset, run on every prompt or model change. The goal is to be able to say “version B scores 4% higher across 120 cases with zero regressions” instead of “version B seems nicer.” Once you can measure quality, you can improve it deliberately and gate deploys on it.
The spectrum of eval methods
Evals range from cheap and deterministic to rich and expensive. Deterministic checks are the cheapest: does the output parse as valid JSON, contain a required field, match an exact answer, or stay under a length cap? Run these first — they catch the most embarrassing failures for free. Reference-based metrics compare output to a gold answer, useful for translation or extraction tasks with a clear correct result.
LLM-as-judge uses a strong model to grade outputs against a rubric — fluency, correctness, helpfulness — at a scale humans cannot match. It is the workhorse of modern eval suites, but it must be calibrated: score a human-labelled subset, check that the judge agrees with humans, and only then trust it at volume. G-Eval is a structured form of LLM-as-judge that asks the judge to reason through explicit criteria with chain-of-thought before assigning a score, which improves consistency. For retrieval systems, RAGAS scores RAG-specific properties — faithfulness, answer relevance, and context precision and recall — so you can localise whether a failure came from retrieval or from generation.
Building a CI eval harness
The payoff comes from automation. Assemble a dataset of inputs with expected properties, write a scorer for each metric, and wrap the whole thing in a script that runs in CI. On every prompt or model change, the harness runs the suite, reports per-metric scores, and fails the build if any score drops below its threshold or regresses against the baseline. This is what turns evals from a one-off experiment into a permanent safety net.
Keep the dataset versioned alongside your code, and grow it the way you grow a test suite: every time a user hits a real failure, distil it into a new eval case so the bug can never silently return. Track scores over time on a dashboard so you can see whether quality is trending up or quietly eroding.
Common pitfalls
Beware of an uncalibrated judge — an LLM grader that disagrees with humans gives you confident, precise, wrong numbers. Avoid datasets that only contain easy cases; your suite should be dense with the hard inputs that actually break the system. Do not over-index on a single average score, since it can hide a sharp regression in one important category. And remember that evals measure what you encode — if a behaviour matters, write a test for it, because anything unmeasured will drift.