ux

Explain the task a product handles well, compare the current alternative and defend one improvement with evidence from actual use.

Chat is the default answer to 'add AI', and often the wrong one. The interviewer wants you to match the interface to each job, and to say where a button beats a conversation.

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.

Users let an assistant act when they can see what it did and take it back. The interviewer wants an activity log, real undo, and clear handling of actions that cannot be undone.

Users either expect too much from AI features and churn after the first failure, or too little and never try the useful parts. Onboarding sets the mental model that decides which happens.

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.

Explicit ratings are rare, biased and vague. The strongest feedback signals are things users already do. The interviewer wants a design that captures them and turns a few explicit reports into labelled examples.

How an assistant fails shapes trust as much as how it succeeds. The interviewer wants different responses for different reasons, each giving users a way forward.

Most AI writing value comes after the first draft. The interviewer wants a design that makes partial acceptance, targeted rewrites and comparison easy, and keeps the user in control of their document.

Streaming is the cheapest latency improvement in AI products. The interviewer wants the reason, the trade-offs, and the cases where showing partial output is a mistake.

Translation is the smallest part of multilingual AI UX. The interviewer wants script choice, code-mixing, voice, cultural norms, and honest handling of languages where quality is weaker.

Delegation is the highest autonomy level and the hardest to design. Principal answers cover the brief, budgets and boundaries, checkpoints, reporting, and what happens when the agent hits something it should not decide.

Two problems hide in one complaint: the agent is slow, and waiting for it feels worse than it needs to. Strong answers attack both, and question whether every task should make the user wait at all.

Models rarely ask unless told to, and research shows asking at the right moment raises success substantially. Ask when the answer changes the outcome and the action is costly, batch questions up front, and measure both kinds of error.