A convincing AI demo takes an afternoon. An assistant that customers rely on takes careful engineering, and the gap between the two is where most AI projects stall.
Ground it in your data. Assistants that answer from your own documents, with citations, are far more accurate and trustworthy than ones relying on general knowledge alone. Retrieval quality matters more than model choice.
Decide the guardrails in writing. What may the assistant never say? When must it hand over to a person? What happens when someone tries to talk it out of its instructions? These are product decisions, and they belong in a document before launch.
Test like you mean it. Keep a set of real questions with known good answers and re-run it after every change. Most regressions come from a small prompt tweak that fixed one case and quietly broke others.
Start narrow. An assistant that handles three tasks brilliantly earns trust and usage; one that attempts thirty and gets half wrong loses both. Expand only when the data says it's working.
