Product design question

How would you improve the restaurant search?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Product sense for a discovery feature where trust and relevance drive conversion.

Pick a segment, for example users searching for a specific cuisine in an unfamiliar area versus users deciding where to eat nearby.
Identify a pain point: search results often prioritize paid placement or generic popularity over genuine fit for the user's specific craving.
Prioritize relevance and trust signals over ad driven ranking, since a bad recommendation directly hurts user trust in the platform.
Propose incorporating recent, verified reviews and a 'why this matches' explanation tied to the user's stated preferences.
Add filters for real constraints, like current wait time or actual open hours verified in real time, not just listed hours.
Define success as click to visit conversion rate and reduction in searches ending without a selection.

Names a specific, real friction point, paid placement crowding out genuine relevance, rather than a vague 'better search'.
Proposes an explainability feature, 'why this matches', which builds user trust in the recommendation directly.
Gives an illustrative number, e.g. assumes 30 percent of searches currently end with no restaurant selected, signaling poor relevance.
Adds real time verified constraints like open hours, addressing a common and frustrating real world failure mode.

Ignoring the tension between ad revenue and genuine search relevance.
Proposing generic filter additions without addressing why current search results feel untrustworthy.
No metric tied to whether users actually found and visited a restaurant.

Chapters of the AI PM course that teach what this question tests.