It worked in the demo. It worked in staging. Your team shipped it, gave each other a nod, and moved on.

Six weeks later, someone changed the prompt. Someone else upgraded the model. A retrieval config got tweaked. Nobody broke anything — but the feature got quietly worse, one response at a time, until a user noticed before you did.
This is the failure mode AI features are built for. No exceptions. No alerts. No obvious moment where something went wrong. Just a slow drift away from the thing you tested and toward something nobody owns.

This session gives you the eval loop that catches drift before your users do. Using a realistic knowledge assistant as the running example, we'll expose three real failure modes — unsupported claims, missed key information, and brittleness after a change — then build a lightweight eval suite that turns each one into a repeatable check. You'll see a compact golden dataset, three scoring approaches, and a regression workflow wired into CI.

No research background required. If you know how to write a test, you already know how to think about this.
Walk away with a practical four-step eval workflow and a template you can drop into whatever AI feature your team is shipping right now