Vibe coder or product engineer interview questions

As asked

You used an AI coding agent to build a settings page and API route in a day. What checks do you perform before you ship it?

Sample answer outline

The candidate should treat AI output as untrusted code that can still be useful. Run the app, read the diff, check data flow, auth, validation, loading states, empty states, errors, accessibility, and telemetry. Tests should cover the contract between UI and API, plus the riskiest edge cases the agent may have missed. Strong answers mention dependency review, secret handling, permission boundaries, and manual product review against the intended user workflow. The main tripwire is shipping because the page looks right once, without understanding what was generated.

Expect these follow-ups

• What kinds of bugs do coding agents most often introduce?
• How do you keep velocity without turning review into a full rewrite?
• What would make you throw away the generated code?

As asked

A founder asks you to 'add team invites and billing seats' using a coding agent. How do you turn that prompt into a plan the agent can execute safely?

Sample answer outline

A strong answer decomposes the request into product rules, data model changes, API contracts, UI states, and migration or billing provider concerns before asking an agent to edit code. The candidate should identify risk areas: authorisation, duplicate invites, race conditions around seat limits, webhook idempotency, and auditability. Good agent instructions include file scope, acceptance criteria, test commands, and explicit non-goals. They should iterate in small diffs and verify each step rather than requesting a sweeping feature in one prompt. Interviewers look for product judgement and engineering control, not prompt bravado.

Expect these follow-ups

• What would you ask the founder before writing code?
• Which part would you implement manually rather than delegate to an agent?
• How do you check that billing webhooks are idempotent?

As asked

Implement a small optimistic UI helper for a feature toggle. It should update immediately, call the server, and roll back if the request fails.

Sample answer outline

The core is to separate the previous state, optimistic state, server confirmation, and rollback path. A good candidate handles concurrent clicks by either disabling while pending or tracking request order so stale responses do not overwrite newer intent. The code should surface an error path the UI can show, not silently swallow failures. Interviewers want pragmatic product engineering: instant feedback without corrupting state. Common mistakes include not restoring the previous value, ignoring rejected promises, or allowing double toggles to race.

Reference implementation (typescript)

type SaveToggle = (enabled: boolean) => Promise<void>;

export async function applyOptimisticToggle(
  current: boolean,
  setEnabled: (next: boolean) => void,
  saveToggle: SaveToggle,
): Promise<void> {
  const next = !current;
  setEnabled(next);

  try {
    await saveToggle(next);
  } catch (error) {
    setEnabled(current);
    throw error;
  }
}

Expect these follow-ups

• How would you handle two rapid clicks before the first request returns?
• What changes if this value is also updated by another browser tab?
• Where would you put analytics for successful and failed toggles?

As asked

You built a customer-facing prototype with AI assistance and users love it. How do you decide what must change before it becomes production software?

Sample answer outline

Start by separating validated product value from implementation quality. The candidate should assess security, auth, data correctness, observability, performance, accessibility, error recovery, testing, and maintainability. Strong answers include a rewrite-or-harden decision based on code shape, product urgency, and the cost of carrying prototype assumptions. They should preserve the parts that created user value while removing brittle shortcuts such as local-only state, hardcoded IDs, or hidden manual steps. The trap is either shipping the prototype unchanged or rebuilding it so slowly that the product learning decays.

Expect these follow-ups

• What prototype shortcut is acceptable for a pilot but not for production?
• How do you communicate the hardening work to a non-technical founder?
• When is a rewrite cheaper than incremental cleanup?