Shipping AI features without the demo trap
A working prototype is easy. Most of the product work is making it survive real prompts, latency, and empty states.
Most AI demos look sharp in a five-minute walkthrough, where the model answers cleanly. Then a real user pastes a messy PDF, asks an ambiguous question, or refreshes mid-stream, and the feature falls apart.
When I build AI into a product, I treat the model as one dependency in a larger workflow. The hard parts are usually around the edges: what happens when the response is slow, when retrieval returns nothing useful, or when the user needs a way to correct the system without starting over.
A practical loop that keeps working for me: define the job in one sentence, constrain inputs, show progress early, and make failure recoverable. If a feature only works on curated happy-path prompts, it is still a demo.