Shipping AI features users actually trust
Grounding, citations, and evals — the practical playbook for putting LLMs in production without the hallucinations.
Shipping an AI feature is easy. Shipping one that users trust enough to rely on every day is a different problem entirely — and it's mostly not a modeling problem.
The first lesson: ground everything. A model answering from its own weights will eventually say something confidently wrong, and one bad answer costs more trust than fifty good ones earn. Retrieval over your own verified content, with citations the user can check, turns 'trust me' into 'see for yourself.'
The second lesson: build evals before you build features. A simple suite of fifty representative prompts with expected behaviors — checked on every prompt or model change — catches regressions that no amount of manual spot-checking will. Treat prompts like code: versioned, reviewed, tested.
Finally, design for the failure case. Show confidence levels, make 'I don't know' a first-class answer, and give users a one-click way to flag bad output. Teams that hide model failures ship slower and lose trust faster than teams that surface them honestly.