AI Evals Explained: Why Product Teams, Not Engineers, Must Define Quality

AI evaluations (evals) are structured systems that track whether an AI product's output quality changes over time, helping teams avoid subjective debates about performance degradation. At their core, evals consist of real user queries, written criteria for what a good response looks like, and a repeatable method to check outputs against those criteria. The central challenge is not technical but organizational: someone must explicitly define what 'correct' means for each use case, including tone, accuracy, and acceptable trade-offs. Experts argue that product managers should own these quality definitions in plain prose, while engineers handle the automated testing infrastructure. Teams that fail to assign clear ownership risk having product judgements made by default, embedded silently into dashboards and test scripts by whoever built them.