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LLM outputs are non-deterministic and open-ended, so 'does it pass the test' has no exact-match answer — which means every prompt tweak you ship on vibes is silently regressing cases you never look at. Evals are the fix: a repeatable, quantified measure of output quality, the way unit tests are for code. Here's the taxonomy, how LLM-as-judge works and where it lies to you, and how to wire it all into a regression gate.