How do I prepare for an AI coding interview?

Ask the recruiter or interviewer for the permitted tools and the task format. An AI coding interview could mean debugging a repository with an agent, building from a brief, or explaining an AI-generated change. Match your practice to the format they describe. If the interview prohibits AI, rehearse without an assistant using the specified format.

• Which assistants, models, editors, documentation and web searches may you use?
• What code or data may you paste into an assistant, and must you use a provided environment?
• How long is the task, and are there token limits or restrictions on copying code?
• What should you explain while working, and which tests or other deliverables are expected?

Start with an unfamiliar issue whose scope you can explain. Read the reported behavior, expected behavior, and relevant repository conventions. Write down a concrete condition that would distinguish a fix from a change that merely hides the symptom.

The repository challenges on PraxisAI come from SWE-bench, a benchmark of GitHub issues used to evaluate model-generated patches. These issues can have public solutions and may be familiar to models. Work from the task's supplied instructions and trace the issue yourself; looking up the reference patch changes what the rehearsal measures.

Use the interview's time limit when you know it. For a 60-minute bug-fix rehearsal, the allocation below is one practical starting point. Adjust it to the task while leaving time to verify and explain the result. Keep an honest record of what you completed within the limit.

In a live PraxisAI round, environment preparation happens before the timer starts. Once the workspace is ready, pressing Start inside the round begins the clock. Live practice depends on a connected runner and available models.

• Minutes 0–10: read the task, find the affected code and try to reproduce the failure. Record the expected behavior and the command you ran.
• Minutes 10–35: give the agent a bounded investigation or edit, inspect its explanation and review each changed file. Tie follow-ups to new evidence.
• Minutes 35–50: run the relevant check and nearby regression tests. Read the output to confirm that tests actually executed.
• Minutes 50–60: inspect the final diff and untracked files, then explain the cause, the fix, the checks and any uncertainty. Submit the work you have.

Find the affected function, its callers and nearby tests before asking for a broad change. Give the agent the failing command, relevant file, expected behavior and constraints you have verified. Ask it to investigate the specific failure and propose a small change consistent with the repository.

Check the result yourself. If a test still fails, identify which assumption the failure contradicts before issuing another request. Inspect the diff for unrelated edits, weakened tests or omitted files. A dependency or collection error calls for a different next step from an assertion failure; record checks you could not run.

Practice a short explanation of the original cause, why the change addresses it, what the agent contributed and which checks support the result. State the remaining risks or untested behavior. You should be able to describe the patch without relying on the agent's explanation.

After the rehearsal, choose one concrete habit to try next time, such as reading callers before the first edit or running a targeted test before a broad suite. Repeat on another unfamiliar task and keep evidence of that action. Task difficulty varies, so a score increase alone does not establish improvement.

These are suggested schedules, not required study durations. Use the time you have to complete and review a small number of representative tasks.

• With one day: confirm the rules, complete one timed rehearsal, review one weak moment and practice explaining the final patch. Make a short checklist for the interview.
• With one week: confirm the format first, complete a baseline rehearsal, practice its weakest step, then try a second unfamiliar task under similar conditions. Compare your decisions and evidence, and review your setup before interview day.

PraxisAI provides timed real-repository bug-fix challenges with an AI agent in browser VS Code, alongside take-home projects built from a brief. A practice round records prompts, agent activity and test evidence so you can review how the work unfolded. Choose the task type that matches the interview format you were given.

Free practice shows a basic score, verdict and grading result. Active plan access, including an eligible complimentary plan, unlocks detailed practice reports with recorded prompts, coaching and replay where the necessary evidence is available. Adding usage credit funds full-model rounds; usage credit alone does not unlock detailed reports. Billing shows your current plan access and payment status. Check the current allowance and next-round model before starting: after weekly free rounds are used, available credit is spent first, with free-model fallback subject to availability and daily limits.

If the grading environment cannot run, the overall score is withheld; inspect the status and available observations. Process scores and behavior reads use rules and heuristics. They cannot establish job performance, guarantee interview success or tell you how a particular employer will evaluate your work.