How to prepare for an AI-assisted coding interview

An invitation that says AI-assisted can still leave you unsure what help is allowed. This checklist helps you settle the tool permissions, working environment, and submission requirements before you build your practice routine.

• What does AI-assisted mean? Confirm whether candidates can use AI, the interviewer uses AI, or both. Ask which specific assistants are approved and whether permission covers autocomplete, chat, automated code changes, or live interview suggestions.
• Where will you work? Confirm the language, runtime, editor, repository access, dependency restrictions, and internet access. Practice running a small program and its tests in the expected setup.
• What can you send to an assistant? Ask whether prompts can include the problem statement, repository code, screenshots, or interview audio. Permission for an editor assistant does not automatically include audio capture.
• What must you show or submit? Clarify whether the interviewer expects screen sharing, prompt history, disclosure of generated code, tests, or a written explanation. Ask what the round assesses rather than assuming prompt quality replaces coding fundamentals.

If the instructions are silent, ask the recruiter before the round. Keep preparation separate from live permissions: practicing with a tool does not establish that you can use it during the interview.

For preparation guidance across coding, behavioral, and system-design rounds, browse the interview types answers.

Choose a problem you can reason through independently, then practice the complete path from requirements to a tested solution. Use the approved assistant and environment where available, and keep some practice unaided so you can explain and modify the result yourself.

• Define the contract first. Write down input and output types, size constraints, error behavior, and examples. For an event deduplication task, decide which duplicate survives and whether output order must match input order.
• State your approach before prompting. Describe the data structure, the rule the algorithm must preserve, and the expected time and space costs. This gives you something concrete against which to review generated code.
• Request a bounded change. Name the language, function signature, constraints, and permitted dependencies. For example: implement the deduplication function, preserve the first occurrence, leave the input unchanged, and explain any assumptions. Review that function before requesting surrounding code.
• Inspect before running. Check loop boundaries, mutation, return values, and unfamiliar API calls. Read the actual diff when a tool edits files. Reject changes you cannot explain.
• Test against your own expectations. Decide expected outputs independently before asking for test code. Cover an ordinary case, a relevant boundary, and a case that distinguishes the intended behavior from a plausible mistake. Check invalid input only according to the agreed contract.
• Explain and modify the result. Walk through a concrete input, justify complexity, and change a requirement without regenerating the entire solution. Passing the current tests does not establish correctness for every input.

For rehearsal of the spoken explanation, visit the mock interview page.

Run a rehearsal using the interview's stated duration. Include reading the task, clarifying requirements, implementing, testing, and a final walkthrough within that time. Reserve time for verification instead of treating it as something to do only if coding finishes early.

Consider a backend engineer preparing for a payments-platform role. During a rehearsal, she requests a function that keeps the first event for each transaction ID in input order, but the generated draft overwrites earlier events in a map. A test with duplicate IDs and different amounts exposes the error; she changes the function to insert only unseen IDs and explains why the earlier event must survive.

Practice narrating decisions at useful moments: before a prompt, when accepting or rejecting a suggestion, and after a test changes your understanding. Explain the requirement, the evidence, and the next step instead of reading generated code aloud line by line.

• Add a follow-up. Change an ordering rule, input size, or memory constraint. Identify which assumptions and tests must change before editing.
• Simulate unavailable AI. Continue from the last understood version using the editor, compiler, debugger, and documentation the interview permits.
• Review the bottleneck. Afterward, identify whether time disappeared into unclear prompts, large generated changes, environment problems, or debugging. Target the next rehearsal at that specific weakness.

Use SubcueAI for live assistance only when the interview permits that form of help and the relevant audio capture. Choose its surface according to where the call runs and which audio your preparation requires.

• Native desktop app: The flagship app runs on macOS and Windows, captures system audio plus your microphone, and displays assistance in a floating local overlay. It works with desktop meeting clients such as Zoom and Microsoft Teams.
• Browser extension: The Side Panel provides live in-interview assistance on Chrome and Edge for browser-tab calls, including Google Meet. It captures only the meeting tab's audio, hearing the interviewer rather than your microphone, and does not transcribe the candidate. The Firefox build is for mock practice only.

Neither surface sends a meeting bot into the call or injects a content script into the meeting page. These properties do not guarantee invisibility. Shared or recorded screens can expose displayed assistance; proctored interviews and company-managed devices are outside the live-assistance scenarios covered here.

Before an approved session, use the setup tutorial to prepare the surface that matches your meeting environment.