prototyping

A new term for a new skill. The interviewer wants a definition, a sensible use for PMs, and an honest view of where AI-built prototypes stop being trustworthy.

The part of the prototyping round that separates candidates. Ten minutes is enough for a small, sharp evaluation if you choose the cases well and grade them pass or fail.

Opinions about prompts are cheap. A small grid of prompts by models on fifty real examples turns a debate into a table, and the interviewer wants to see you build it.

Text-to-SQL demos are easy and dangerous. The interviewer wants the controls that stop wrong numbers, data leaks and runaway queries, and a design that makes answers checkable.

Real data makes prototypes honest; careless handling makes them incidents. The interviewer wants a path that gets realistic evidence without leaking customer data into unapproved tools.

Prototypes tested in English hide most of what goes wrong for users in other languages. The interviewer wants a prototype cycle that tests real speech and text from target users, with native evaluation.

A working prototype is the start of the decision, not the end. The interviewer wants evidence on value, feasibility at real quality, cost, risk and strategic fit, and a decision rule set before the evidence arrives.

A practical question about the PM toolkit. The interviewer wants tool categories matched to the question you're trying to answer, not brand loyalty.

A leadership question about changing how a team works. The answer needs skills, tools, safe data, rituals and incentives, plus guardrails so prototypes do not turn into unreviewed production code.

A portfolio process question. Prototypes make AI ideas cheap to test; the challenge is turning a stream of demos into consistent, evidence-based funding decisions.

Interviewers want a workflow, not a tool name. The scored answer shows the instruction file, the plan review, the permissions and the evaluation that make an afternoon's prototype worth trusting.

The tool choice is the easy part. Interviewers screen for how you decide on evidence, what you allow near code and data, and how you stop prototypes becoming unowned production code.

An instruction file is a short brief every coding agent reads before it starts. The answer that scores keeps it short, puts the commands and the never-do rules first, and explains each line.

A person behind the curtain answers whether users want it; a real model answers whether it works. Pick by the uncertainty you need to resolve, and never fake it with paying users without telling them.

Six sessions find usability problems; they cannot estimate output quality. Fix the tasks, log every output, separate interface, quality and latency findings, and claim only what six people can show.

Six configurations and a dozen sessions leave two sessions per cell. Keep the findings that hold across versions, drop comparisons between them, and stamp every future session with its full configuration.

Companies that shipped MCP servers exposed reads widely and held back money-moving actions. Start with a few consolidated read tools, test descriptions against real tasks, and gate anything irreversible.

Two minutes rewards one finished user journey and an honest failure. Confirm the AI-tool rules first, build one end-to-end job with a small eval, and spend the video on the problem, the demo, the failure and the next step.

The test is choosing what to build, not building fast. Name the riskiest assumption in your own case, build only the slice that tests it and say what result would change your recommendation.

Showing unreliable flows as if they work creates expectations you cannot meet. Demo the reliable two live, show the other four as clearly labelled concepts with their status, and never imply availability.