trust

An accessibility prompt that tests empathy and judgment together. The design is easy to sketch; the hard part is deciding what the system must never guess about.

An emotionally loaded design question. The interviewer is watching for a product that actually helps, and for the judgment to see where companionship turns into dependency, manipulation or missed emergencies.

The failure every legal AI product fears. The interviewer wants a response that makes invented citations structurally impossible to present as real, not a promise to improve the model.

Summaries shape purchases strongly, so their framing matters. Summarise only themes several verified reviewers share, show counts and the balance of views, link to the source reviews, and test for sentiment drift before release.

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.

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.

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.

'Creepy' means the system knows more than users expected; 'random' means they cannot see the logic. Both are fixed with honest explanations and real controls, not marketing copy.

Health assistants are useful exactly where they are risky. The interviewer wants clear tiers of what the assistant answers, how it handles emergencies and personal medical advice, and an experience that stays helpful instead of hiding behind disclaimers.

Trust is easy to survey and hard to measure. The interviewer wants a composite built from behaviour, interpreted carefully, and validated against outcomes the business cares about.

Personality can make an assistant pleasant or manipulative, memorable or annoying. The interviewer wants a personality that serves users' goals, stays honest about being an AI, and adapts when the moment is serious.

Inconsistent AI behaviour across products confuses users about what to trust. Principal answers create shared patterns and principles that teams adopt because they are useful, with room for product-specific needs.

Users rate flattering answers higher, so the launch metric points the wrong way. Build the measurement that catches it and decide what blocks a launch.

Hiding the bot is off the table in many markets and was never a good bet. Disclose on the first turn, then win back the lost sales by testing how you disclose and what the assistant does next.

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 pricing changes that caused backlash were mostly communication failures. Say who is affected, show a meter in units people understand and never cut off work mid-task.

Separate sponsored placement from answer quality, account for publisher incentives and calculate revenue per eligible query.

Translation looks solved until a price, a date or a promise is mistranslated between two strangers doing business. The design centres on speed, protecting critical details, and letting both sides see the original.

Assistant users buy 60% more often, which proves almost nothing. Ground every price in live data, separate paid placement from advice and measure lift with a holdout.

A product design question where the hard part is not the model. It is deciding which actions the agent takes alone, which it drafts, and how the user learns to trust it.

When the metric is the invoice, every edge case becomes a billing dispute. Principal-level answers design a definition customers can audit and a price that survives the worst month.

A transparency question with a regulatory core and a design challenge. The interviewer wants the rules stated carefully, and disclosure that informs users without cluttering the product.

The data flywheel is real, and so is the trust cost of getting consent wrong. The interviewer wants a decision framework: legal basis, contracts, user choice, de-identification and the value actually expected.

Whether or not the fear was accurate, the trust problem was real. Show a fast plain-language answer, a binding commitment and a product change that made the answer checkable.