Google AI Product Sense interview questions

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 growth prompt with a hard constraint. Three engineers rules out most ideas, and doubling usage also doubles the compute bill. The interviewer wants a funnel diagnosis and two or three bets that fit.

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.

Often the first question in an AI PM loop. Generic answers ('AI is uncertain') score poorly; concrete changes to specs, testing, cost and launches score well.

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

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.

A craft question that separates PMs who have shipped AI features from those who have read about them. The answer is a document organised around failure modes, thresholds and evidence.

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.

A principal-level design and strategy question. Every default assumption (literacy, a good phone, fast data, English, a credit card) fails. The answer has to pick a job worth solving and an economic model that survives.

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.

Low adoption has four usual causes: people don't see it, don't need it, try it and don't trust it, or find it in the wrong place. Each has a cheap test, and the interviewer wants you to run them before redesigning.

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 portfolio question for a company that already uses machine learning everywhere. Strong answers find where generative AI changes what users can do, not just how recommendations are computed, and weigh it against costs to artists and licensing.

A convincing field score can conceal a wrong purchase order. Define the complete unit of work, preserve page and row evidence, and calculate the review queue before choosing autonomy.

Separate new-user and new-item problems, design a useful first session, and test whether early relevance survives beyond onboarding.

Choose a human benefit, distinguish people from agents and avoid metrics that reward endless synthetic activity.

Move beyond polished application text to evidence of job-relevant ability, while accounting for candidate burden and reviewer capacity.

A prioritization score favors automated triage, but the data rights are unresolved. Separate a delivery commitment from a small experiment.

The hardest part is timing: people must record while they can still speak clearly. Reach them at diagnosis through clinicians, keep recording short, protect the voice, and plan for speech that is already changing.

A read-aloud coach that listens and helps can raise reading levels, as one large pilot found. Work offline on a fixed library, process voice only to respond in the moment, and measure reading gains against a control.

Speaking is the skill learners avoid and value most. Design short, scenario-based calls at the learner's level with one correction per turn, and move it into the main paid plan once the cost per minute allows.

A shared device has several users, some of them children, and no screen to confirm on. Lead with household coordination, make voice recognition optional, gate purchases and keep proactive prompts for the person they concern.

Continuous vision is useful and alarming to everyone the wearer looks at. Allow live vision only in sessions the wearer starts, enforce a visible indicator in hardware, refuse to identify people and lead with accessibility.

People mostly find their own files by navigating, using search as a last resort. Keep navigation strong, bring related notes up while people write, and offer cited Q&A for when they can't remember where something is.

The question behind every AI product decision: a repeatable test, applied step by step, and the willingness to say 'a rule does this better' when it does, and 'a model, with a reviewer' when it does not.

Tenfold growth is not an optimisation problem. The interviewer wants to see you find new users the product does not serve today, and protect reliability and margins while doing it.

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.

The most common story in AI product development. A demo proves the model can do something; a product needs it to do that thing reliably on inputs nobody chose. The strong answer separates those milestones and resets expectations without losing the sponsor.

A design question with a regulatory constraint arriving halfway. The interviewer wants a useful product on both sides of it, and a clear line between preparing a decision and making one.

A strategy-sized design question. The tempting answer replaces the search bar with a chatbot. The scoring answer keeps fast keyword search for the shoppers who know what they want and uses AI for the shoppers who don't.