Evals Aren’t a One-Time Report: Build a Living Test Suite That Ships With Every Release.

Continuous evaluation in production (monitoring, regressions, evals in CI/CD) You finally shipped that generative AI feature, and the initial manual testing looked spectacular. A few weeks later, users start complaining that the system is hallucinating, dropping context, or responding with a completely different tone. You haven’t changed the model, but the underlying API provider updated their weights, your retrieval corpus grew, and user prompts evolved. Traditional software engineering relies on deterministic unit tests to catch regressions before they hit production. AI engineering, however, often relies on static, one-off evaluation spreadsheets that age out the moment a model is deployed. This gap between traditional Continuous Integration/Continuous Deployment (CI/CD) and AI evaluation is the root cause of silent degradation in production systems. In this article, you will learn how to shift from manual vibe checks to a continuous evaluation paradigm. We will explore how to integ