Why evals break the moment a model hits production

Most teams ship a model after it clears a benchmark suite, then watch quality quietly erode over the following weeks. The gap isn't the model — it's the mismatch between the distribution it was evaluated on and the distribution it actually serves.

The fix starts with building evaluation sets from your own production traffic, not a public leaderboard. We pull failure cases directly from logs, cluster them by root cause, and turn the worst clusters into a standing regression suite.

Once that's in place, every fine-tune or prompt change gets checked against the same failures that hurt you last time — so regressions get caught before a customer finds them.