Replit Live Prototyping & AI-Native Workflow interview questions

Rehearse a 40-minute build with time reserved for testing. Choose a small user task, make it work and explain the failures you found before proposing a launch.

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.

Things break in live rounds, and interviewers often learn more from how you recover than from a smooth build. The answer is calm narration, a time box, simplification and a fallback that still shows judgment.

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

Asked of senior candidates who will run these rounds, and useful for anyone preparing. Knowing the rubric from the interviewer's side shows what to optimise for.

A coding agent builds the wrong thing quickly from a vague brief. The answer that scores shows what a PM adds to a PRD before handing it to an agent, and where they review.

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.

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 tenth of apps in one audit exposed their database to anyone. Check access control on every table, keys in the client, auth with a second account and an owner for the findings before any customer sees the link.

Models sometimes invent package names, and attackers register them. Keep prototyping, but allow-list common libraries, check that new packages exist and are established, and install in a sandbox with no secrets.

A worked live-round walkthrough. The trap is building a pretty summariser. The scoring path is a narrow extractor with structured output, a small test set that probes the hard cases, and an honest readout.

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.

The handoff is where many AI prototypes die or get rebuilt wrongly. The interviewer wants the artefacts that carry learning forward and a clear list of what the prototype did not prove.

AI can cluster thousands of comments in minutes, and it can also invent themes, flatten detail and overweight loud voices. The interviewer wants speed with traceability back to real quotes.

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.

Synthetic personas are cheap and fast, and they tell you what a model thinks people might say. The interviewer wants a clear line between useful applications and evidence they cannot provide.