If you've prepared for a traditional coding interview, you know the drill: grind problems, review solutions, build pattern recognition. The core of that advice still holds. But preparing for an AI-enabled interview adds a layer that most prep advice hasn't caught up with — because the environment you practice in now matters as much as what you practice.
The single most effective thing you can do is one full timed practice run. Not a hint of one, not a warm-up: a real, timed, end-to-end run in an environment that matches your interview. Here's the loop that transfers.
The two practice formats
Practice should mirror the format you're facing, because the two formats train different reflexes.
Structured. The company provides the environment, the problem panel, and the AI. You work in a constrained in-browser editor — the same shape as CoderPad and HackerRank. The goal is to make a constrained, unfamiliar AI feel normal well before you're using one for real. Most candidates have never touched an interview-sandbox AI, and its quirks — how it formats output, what it does with a long prompt, how it handles the missing editor shortcuts — are a surprise only the first few times.
Open-ended. You share your screen and use your own editor and your own AI tools. This is a bring-your-own-environment build, and preparation means rehearsing with your tooling, not a generic one: your keybindings, your terminal, your AGENTS.md, the exact command you run to test. The interviewer is watching how you drive your own stack, so familiarity with it is the whole ballgame.
The practice loop
Work through the phases under time, and do it in the format you'll face:
- Orient. Read the codebase or the problem statement, form a map of what exists, and decide what you're going to build. In an open-ended interview, this is where reading the repo's structure matters.
- Plan. Write the approach down before you open the AI chat — decomposition, data structures, sequence of work. Decide before you prompt.
- Drive. Prompt at the right granularity: a clear what-and-how, with the approach pinned by you. Let the AI handle syntax.
- Verify. Run the code after every generation. Read the output as it lands. Use tests as the target you hand the model.
- Communicate. Narrate intent before prompting, review output out loud, and use the AI's wait time to show your reasoning.
A single timed run through all five under pressure surfaces exactly where you're weak — usually either at planning (you skip it) or verification (you trust the output).
Rehearse with a weaker AI
Here's a counterintuitive tip that pays off: practice with a weaker or unfamiliar model than the one you'll use in the interview. If your interview assistant is stronger and more compliant than the tool you trained on, the interview feels easier than practice. If you only rehearse with your best tool, an unfamiliar or constrained AI — which is exactly what a structured interview hands you — will feel sluggish and you'll be fighting it instead of driving it.
Use the interview assistant as the ceiling, not the floor. Training below it is insurance.
Have your environment ready before the day
For an open-ended interview, preparation is partly environment setup:
- AGENTS.md. This is the file that tells the agent your standards at the start of every session. Having one that documents your conventions — testing command, formatting rules, file layout — means the AI starts aligned with how you work instead of guessing.
- Test commands. Know the exact command that runs your test suite cold. In an interview you don't want to be discovering your test setup in front of the interviewer.
- A clean, minimal project. Don't burn orientation time on a messy repo. The interviewer wants to see you build, not watch you untangle a workspace.
Rehearse the mechanical bits
It sounds small, but rehearsing how you share a screen, switch windows, and run a command under someone's watch removes a layer of nervousness. The mechanical is part of the performance.
The day before
The day before is not for new material. It's for:
- One last full run in your target format, timed, to keep the workflow warm.
- Environment verification. The repo builds, the test command works, your tools log in.
- Rest. Interview performance is heavily affected by fatigue, and an AI-driven interview is more demanding than a traditional one because you're carrying two conversations. A clear head is an advantage.
The short version
The core skills haven't changed — planning, problem decomposition, clean code, verification. What's new is that you must practice them in the AI-enabled environment you'll face, under time, until the workflow is automatic. One timed run in the right format beats a week of untimed problem grinding, because it trains the judgment that the interview actually measures.