verification

The central AI UX question. The interviewer wants patterns that make errors cheap to spot and cheap to fix, sized to what an error costs.

Accuracy and trust are different things. The feature saves no time if people double-check everything, and the fix starts with finding out why experts are cautious.

Citations are the main trust device in AI answers, and published audits of shipped search engines find many that do not support the sentence they sit on. The interviewer wants citations at the right granularity, checkable in seconds, and honest about what they support.

Trust that outruns accuracy is as dangerous as distrust. The interviewer wants design that keeps experts engaged where it matters, without making the tool tedious.

Models sometimes describe actions they never took. Let the system decide what is done from checks it runs itself, and show users the evidence.

The compounding arithmetic every agent PM should be able to do in their head. Once you show 0.95 to the tenth power, the interviewer wants to know which of four levers you would pull first.

Self-declaration does not count in the UK, and US courts have upheld state age-verification laws. Combine age estimation with step-up verification, default to the safer experience and block where no compliant method exists.

The part of the prototyping round that separates candidates. Ten minutes is enough for a small, sharp evaluation if you choose the cases well and grade them pass or fail.