Instructions
Trust is the foundation of any AI product — and it's harder to earn with AI than with conventional software. Users have legitimate reasons to be cautious: AI makes mistakes confidently, it's often opaque about how it works, and the consequences of a wrong output in a professional context can be real. Designing for trust isn't a nicety; it's what determines whether people will actually rely on your product.
The common mistake is conflating confidence with trust. Some builders try to make the AI seem more authoritative — presenting outputs without caveats, hiding uncertainty, omitting the model from the interface. This might feel like it builds trust in the short term, but it backfires the first time users encounter a significant error. Real trust is built through honesty: being...
