Shipping AI Features Users Actually Trust
Grounding, evaluation and UX patterns that turn a demo into a dependable feature.
Kabir Shah · · 7 min read
An AI demo takes an afternoon. An AI feature people rely on every day takes discipline. The gap between the two is almost always the same three things: grounding, evaluation and honest UX.
Ground every answer
Language models are fluent, not factual. The fastest way to earn trust is to answer from your content: docs, tickets, product data. Retrieval-augmented generation does this, but only if retrieval is good. We spend more time on chunking, metadata and ranking than on prompts.
Show your sources. A small “based on 3 articles” link does more for confidence than any disclaimer.
Measure before you ship
Before launch we build an evaluation set from real questions, typically a few hundred, each with an expected outcome. Every prompt or model change runs against it. This turns “it feels better” into a number, and it catches regressions before customers do.
Useful things to track:
- Answer accuracy against the expected outcome
- Refusal rate, so the feature declines when it should rather than guessing
- Latency and cost per answer
Design for being wrong
Even good systems get things wrong. The UX should make that cheap: easy hand-off to a person, a quick way to flag a bad answer, and clear wording when confidence is low. Users forgive mistakes; they don’t forgive being misled.
Start narrow
The best first AI feature solves one high-volume, low-risk job really well. Prove value there, then widen the scope with the evaluation suite as your safety net.
If you’re weighing up where AI fits in your product, we can help you scope it.