It's never been easier to demo an LLM feature and never been harder to ship one users trust. The reason is simple: demos optimise for a single happy path, while production must handle the long tail of messy, ambiguous inputs without hallucinating.
Ground everything in your data
The most reliable way to make an AI feature trustworthy is to stop it free-styling. Retrieval-augmented generation ties every answer to source documents the user can inspect. If the answer claims to be grounded, every claim should carry a citation.
Evaluate like it's a product
- Build a golden set of representative questions
- Score retrieval quality (did we fetch the right sources?)
- Score answer quality (is it correct, cited, complete?)
- Track cost and latency as features, not afterthoughts
If you can't measure whether the system improves or regresses, you don't have a system — you have a demo you re-run every morning.
Guardrails before go-live
Add refusals for out-of-scope topics, confidence thresholds below which the system admits it isn't sure, and human escalation for anything sensitive. The best AI feature is one that knows when to say 'I don't know'.