What is LLM evaluation and why does it matter?
LLM evaluation is the practice of measuring a language model system's output against a fixed set of test cases with known-good answers. Because the same input can produce different outputs on different runs, evaluation is the only reliable way to tell whether a change improved a system or quietly broke it.
Conventional software is verified by tests that pass or fail deterministically. A language model gives a different answer to the same question twice, so correctness has to be measured statistically across a representative set of cases rather than asserted on any single one.
Without evals, teams ship on impressions. A prompt is edited, a handful of examples look better, and the change goes out — along with a regression on a category nobody re-checked. An eval suite turns that into a number that moves up or down, which is what makes iteration safe.
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