What problem should offline evals solve before you ship an AI feature?

Example Answer

The way I'd think about it is this: Offline evals should answer one practical question before launch: if we expose this behavior to users, do we understand the likely failure modes well enough to be confident we are improving the product rather than gambling with it. I do not use them as a vanity score. I use them as a release filter.

A good offline suite should represent the real jobs the feature is supposed to do, the main ways it can fail, and the cases where the product should refuse, escalate, or ask a follow-up instead of bluffing. That includes both normal traffic and the ugly edge cases that matter operationally.

The other thing offline evals solve is debugging leverage. If a change regresses, I want to know whether the problem is safety, grounding, tool use, latency-sensitive behavior, or workflow completion. If the eval cannot tell me that, it is not pulling its weight.

Common Poor Answer

A weak answer is saying offline evals are mainly for checking whether the model is "smart enough." That is too vague and ignores release safety, failure coverage, and debugging value.