product design

A classic AI product sense prompt. The trap is a chat box bolted onto a grid. The strong answer picks a user, runs every job through an AI-fit test, and treats a silently wrong number as the main risk.

A product design question where the obvious feature, answering homework questions, works against the user's real goal. Strong answers design for learning, for the exam, and for the phone the student actually has.

Small sellers have little time, uneven digital skills and thin margins. The strong answer finds the two jobs where AI changes their week, and avoids an assistant that sounds helpful and does nothing.

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.

The user is driving. Every design choice follows from that, and candidates who sketch a chat screen fail the question in the first minute.

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.

A shared vocabulary for one of the biggest decisions in AI product design: how much the AI does on its own. The interviewer wants the levels, and the rule for choosing among them.

Everyone knows models make things up. The interviewer wants to hear why, which kinds of hallucination exist, and a layered set of product defences, without promising to eliminate it.

Models that take images, audio and documents open new inputs and new failure modes. Explain what they do, what they cost, and the product decisions that change.

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.

Research agents are judged on whether an analyst can trust and reuse the output. The design centres on source quality, verifiable citations and showing the plan before spending ten minutes.

A principal-level platform design. Customers want better results on their tasks; most of them would get there faster without fine-tuning. The product has to guide them, protect their data, preserve safety, and survive base model upgrades.