A convincing AI demo takes an afternoon. A production assistant that customers can't break takes far longer, and the gap between the two is where most AI projects quietly stall. Here is where the real work actually goes.
Tool design eats more time than prompting. An assistant is only as good as the actions it can take, and every tool needs crisp inputs, unambiguous failure modes, and a description written for the model, not for your API docs.
Guardrails are product decisions wearing engineering clothes. What may the assistant never say? When must it hand off to a human? What happens when a user tries to talk it out of its instructions? Decide these explicitly, in writing, before launch.
Testing is the part everyone knows about and almost nobody does, and it's where a QA mindset pays off. Keep a set of real conversations, grade them on 'did it actually resolve the thing,' and re-run them on every prompt change. The most common cause of regressions is a tweak that fixed one conversation and silently broke forty others.
The interface carries more weight than the model. Streaming responses, a clear 'here's what I can help with' opening, and a one-click path to a human set the expectations that decide whether users find the same model helpful or frustrating.
Start narrower than feels impressive. Ship an assistant that handles three intents well before one that handles thirty badly. Nova, the concierge in the corner of this site, follows exactly those rules: scoped, streaming, honest about its limits.
