Building AI Features That Users Actually Trust
Trust is the thing that turns an AI demo into an AI product. Here's how we engineer for it.
It's never been easier to ship an AI feature, and it's never been easier to ship an AI feature that no one trusts. The difference between the two is rarely about model quality — it's about engineering decisions made around the model.
First: be honest about uncertainty. Surface the model's confidence, link to the underlying source whenever possible, and design the UI so that users can challenge the output. Users tolerate AI that's wrong far better than they tolerate AI that's wrong AND confident.
Second: make the loop observable. Every AI feature we ship has structured logging of inputs, outputs, citations and user feedback. That's the data that closes the loop between 'we shipped it' and 'we're actually improving it'.
Third: give users an off-ramp. Every AI-driven action should have a frictionless way to override or disable it. The teams that earn the most trust are the ones most willing to be told 'no'.