An AI demo takes an afternoon. An AI feature that holds up in production is a different discipline entirely.
Treat the model as unreliable infrastructure
Models time out, hallucinate, and change. Validate outputs, constrain them with schemas, and always have a fallback path.
Evaluate continuously
Build an eval set from real inputs and score every change against it. "It felt better" is not a release criterion.
Control cost and latency
Cache aggressively, pick the right model per task, and stream responses so the experience stays fast.
Need AI features that survive contact with real users? We've shipped this before.