The Gap Between Demo and Production

A demo works when everything goes right — the prompt is crafted, the input is clean, the model cooperates, and someone is watching. Production is different: diverse users send unexpected inputs, the model sometimes returns nothing useful, costs add up at scale, latency that felt acceptable in a demo becomes a user experience problem, and failures happen when no one is watching.

The single most important mindset shift for production AI: treat the LLM as an unreliable external service, not as a deterministic function. The same prompt can return different results. The API can fail or rate-limit. The model can be updated and behave differently. Everything downstream of the model call needs to be designed with that unreliability in mind.

Most of what makes an AI feature production-ready is invisible to the end user: graceful error handling, fallbacks, cost controls, eval suites, monitoring, and logging. Building these isn't glamorous, but it's the difference between a feature that stays on and one that gets turned off after a week.