Eval

Without evals you do not know whether your AI feature works well, you only know that it returns something. An eval set is a collection of realistic examples with the desired result, with which you objectively measure whether the system is right, and whether a change improves something or quietly breaks something else.

Checks can be hard (is the amount exactly right), judged by a model (is this answer helpful and tidy), or reviewed by a human for the borderline cases. Together they give a score that you rerun with every change.

Honestly: evals are the most underrated part of most AI projects. Everyone wants to build, almost no one wants to measure. That is precisely why it is our dividing line between a demo that "mostly works" and a system you dare to run in production. Build the eval set from day one.