Shipping AI without evaluation is shipping a bug you cannot reproduce. Models hallucinate, drift, and slow down under load. Users forgive a slow page once; they do not forgive wrong legal summaries or incorrect payout explanations.
Before launch we build a small golden set: real-ish inputs and expected outputs reviewed by the founder or domain expert. We run regression checks on prompt or model changes. We define fallbacks — show manual copy, route to human, degrade gracefully — when confidence is low or latency spikes.
Production monitoring for AI is different from uptime checks. Log prompts and outcomes with privacy in mind, track thumbs-down feedback, and review weekly. The first month tells you more than any benchmark score.
This discipline fits an MVP timeline when scoped early. One feature, one eval set, one owner. AI becomes reliable product surface area — not a lottery ticket tied to your launch press release.