Building AI Features That Survive Production
AI features become valuable when they are treated as part of the product system, not as a thin layer around a prompt. The useful work starts with clear user intent, explicit boundaries, stable input and output contracts, and a plan for what should happen when the model is uncertain or unavailable.
I like to design these features around measurable behavior. That means structured outputs where possible, narrow tools for external actions, test cases based on realistic examples, and enough logging to understand decisions without exposing sensitive data. The goal is not to make the model seem magical. The goal is to make the system dependable.
A production-ready AI workflow should have fallback paths, human review where risk is high, and monitoring that shows whether the feature is actually helping users. When those basics are in place, LLMs can become a practical capability inside the software instead of a fragile demo.