How to ship AI features that survive production

AI demos are cheap. Production AI is a systems problem. The feature needs authentication, rate limits, data boundaries, evaluation, cost ceilings, and a fallback path when the preferred model is slow or unavailable.

Start with the workflow, then build an eval set from real examples. If the model cannot improve the workflow under measurable constraints, it is not ready to be a product feature.

For regulated environments, log every inference with model, version, prompt, retrieved context, and user scope. That audit trail is what lets product teams learn without creating compliance risk.