Evals Are Your Test Suite Now
Unit tests don't cover AI behavior. If you're shipping models without eval suites, you're shipping blind.
You wouldn’t ship a web app without tests. So why are you shipping models without evals?
Unit tests verify deterministic logic. Evals verify probabilistic behavior. Different mechanism, same principle: know what you’re shipping before you ship it.
What an eval suite actually looks like
- Baseline comparisons — is v2 actually better than v1, or just different?
- Regression detection — did fixing hallucinations on topic A break accuracy on topic B?
- Edge case coverage — the inputs your users will find that you didn’t think of
- Latency and cost tracking — because a model that’s 5% better but 3x slower isn’t better
The pattern we see constantly
Team builds a model. Demo looks great. Ships to production. Users hit edge cases within hours. Team scrambles. Hotfix makes it worse somewhere else. No one knows what “good” looks like because there’s no baseline.
This is a solved problem. You just have to treat it like one.
Start here
Pick your 50 worst production inputs from the last month. Write expected outputs. Run every model change against them before deploying. Congratulations — you have an eval suite.
It doesn’t need to be fancy. It needs to exist.
If your deployment process is “vibes look good, ship it” — that’s not a process. That’s a prayer.