HarveyAI Product Manager interview preparation

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.

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.

The strategy question every AI application company faces. 'Better prompts' is not an answer. The interviewer wants a ladder of defensibility and a plan to climb it.

A positioning question where horizontal products face the model providers head-on and vertical products face smaller markets. The interviewer wants a framework and a choice.

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.

Many enterprise RAG failures happen before retrieval: the system never read the document correctly. The interviewer wants you to find the parsing problem and fix it at the right layer.

A lawyer will not trust a summary. Design clause extraction, playbook comparison and cited deviations, with a measured miss rate on the clauses that carry liability.

A stakeholder question wrapped in an agent question. The strong answer protects the deal and the customer by turning a capability promise into an outcome commitment with a path.

95% agreement sounds like a yes. On its own the number says almost nothing, and the thirty-second arithmetic that proves it is what separates a launch gate from a dashboard.

The cost of the model is rarely the biggest number in this decision. The cost of a wrong answer is. One calculation makes the choice obvious in some products and wrong in others.

Overpromising is common in AI because demos impress and capabilities move fast. Panels want to see you reset expectations with evidence, protect the relationship, and fix the process that caused it.

Custom models are sometimes right and usually premature. Show a ladder of cheaper options, the evidence that would justify climbing it, and how you kept the customer while saying no.

Decide which support tasks an agent can safely own. An 82% pass rate needs a denominator, a breakdown of failures and a comparison with today's service.

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.

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.

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.

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.

A deceptively simple design question. Numbers feel transparent, but most users cannot act on them, and the numbers are often less meaningful than they look.

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.

Approval screens fail in a predictable way: people click 'approve all'. The interviewer wants a design that focuses attention on the changes that matter and makes careful review fast.