Language models are remarkably good at sounding right. That fluency is both their power and their risk: a confident paragraph reads the same whether it is accurate, partly invented or simply out of date. Designing AI products is largely the work of making that difference visible.
Show your sources
When an answer is grounded in documents, the interface should make those documents one interaction away. Inline citations that open the exact passage turn a black box into something a user can audit in seconds.
Make uncertainty legible
Not every answer deserves the same visual confidence. Typography, colour and language can signal when a response is well supported, when it is an inference, and when the system simply doesn’t know. Saying “I couldn’t find this” is a feature.
- Distinguish retrieved facts from generated reasoning.
- Offer the user a way to correct or refine the context.
- Keep a visible history so decisions can be revisited.
The goal is not an AI that is always right. It is an AI whose rightness you can check.
Design for latency
AI responses take time. Streaming structure before detail, showing which sources are being read, and letting users interrupt all make waiting feel productive rather than empty.
Evaluate continuously
Models change and data drifts. Products need evaluation suites — real questions with expected qualities — that run before every release. Trust is earned in the interface and protected by the process behind it.