Continuous improvement for AI systems is the practice of improving AI quality through ongoing measurement, not one-time launch testing. It treats evals, traces, human labels, datasets, and experiments as part of the production lifecycle.
This matters because AI systems are non-deterministic and context-dependent. A version can pass a demo, pass a benchmark, and still fail on real user traffic. Continuous improvement keeps production examples flowing back into the system so teams can catch regressions, expand coverage, and tune what actually affects users.