The dirty secret of production AI is that the model is rarely the bottleneck. The bottleneck is the evaluation harness — the regression suite that catches the model silently getting worse on the cases you care about.
Most teams skip this step. They ship an AI feature, watch a few hand-picked examples in a notebook, and call it done. Then the model provider ships an update and the feature starts hallucinating in production, and everyone acts surprised.
Here's how we build evaluation harnesses at Aightify.
Start with a held-out set that looks like production.
The most common failure mode we see is evaluation sets that are too easy. They look great in CI because they're all the cases the model handles well. The moment you ship to production, you find out about the cases that matter and weren't in your eval.
We start every engagement by collecting 200-500 real examples from production logs (anonymised and consent-checked) before writing a single line of code.
Run evals in CI on every prompt change.
Every prompt change, every model swap, every tool addition runs the full eval suite. If accuracy regresses on a slice of cases we care about, CI fails. This sounds obvious but it is rare in practice — most teams gate evals on quarterly reviews.
Treat evals as a product.
Evaluations need owners, dashboards, and quarterly reviews. We track which cases are most often regressed on, which cases we keep missing, and how the eval set is drifting from real production traffic.
The boring investment compounds.
Six months in, our AI engagements have eval suites that catch regressions within hours of a model change. The teams without them find out about regressions from end users.
Eval infrastructure is the highest-leverage work in AI. There is no glamour in it. It pays back every quarter.