If you've spent serious time in experimentation, you already understand LLM evals better than most AI engineers. You just haven't been told yet. I've been running A/B tests for enterprise teams for years. Last year I started building agents in earnest. And somewhere around the third time an "improved" prompt made things quietly worse in production, I had the realization: the eval problem and the experimentation problem are structurally identical. Teams are reinventing controlled comparison, doing it badly, because nobody told them they'd been here before. The Match Nobody Is Pointing Out Here is what an eval actually is. You have a baseline: your current prompt, model, or agent configuration. You make a change. You want to know whether that change made things better or worse. You need a consistent way to measure "better." That's an A/B test. Exactly that. The golden dataset is your holdout test set. The eval judge, human or LLM, is your me...