AI-Allowed Coding Interview Rounds: How to Practise

A few companies now run AI-allowed coding interview rounds: you get an AI chat assistant inside the interview editor, and you are expected to use it. That sounds like it should make the round easier. Candidates who have sat these rounds mostly say it doesn't.

The reason is that the round is not testing whether you can get an AI to write code. It is testing whether you can stay in charge of a codebase while an assistant helps, and whether you catch it when it is wrong.

This guide covers what is publicly known about these rounds, what interviewers seem to grade, where candidates report struggling, and a practice routine you can run on any problem. One caveat up front: almost none of this is officially documented by the companies. Most of it comes from candidate reports and prep sites. Policy varies by company and round — ask your recruiter.

What is an AI-allowed coding round?

It is a live coding interview where an AI assistant is part of the provided environment, with the interviewer watching how you use it. It is different from a take-home where you can use whatever tools you like, and different from a normal round where any AI use is off limits.

Meta's AI-enabled coding round

This is the best-documented one, though still mostly through candidate reports. According to Hello Interview's write-up:

It runs about 60 minutes in CoderPad, with a file explorer, an editor and an AI chat panel.
You work on a multi-file project with existing classes and data models you did not write.
The AI can see the project files but only answers in chat. You write or paste the code yourself.
The round moves through phases: fix a bug, implement a feature, then optimise when larger test inputs expose a slow solution.
Candidates can pick from several models. The exact list differs between sources, so don't rely on any one.

Exponent's guide, republished by the University of Miami careers blog, says Meta began this format in October 2025 and that it replaces one of the two onsite coding rounds, with one classic algorithm round remaining. Meta has not published an official guide that we could find.

Google's Gemini-assisted code comprehension round

In May 2026, Business Insider reported on an internal Google document describing a pilot "code comprehension" round in which candidates read, debug and optimise existing code with Gemini as the approved assistant. Coverage that cites that report, such as Moneywise on Yahoo Finance, says the pilot covers junior and mid-level roles on select US teams, with interviewers assessing "AI fluency, including prompt engineering, output validation and debugging skills".

Sources disagree on the details:

Final Round AI calls it a May 2026 pilot for L3/L4 roles in Cloud and Platforms and Devices, replacing one traditional coding round, with a 12 to 18 month rollout.
The Moneywise piece says the pilot starts in the second half of 2026 and does not say what it replaces.
The Exponent guide says the only round explicitly replaced is a separate open-ended engineering round for earlier-career candidates.

If you are interviewing at Google, assume nothing and ask which format your loop uses.

Other companies

Candidates on r/leetcode have described AI-enabled or AI-assisted rounds at Atlassian, Microsoft and in an Amazon online assessment. These are individual reports, not company policy. The same companies may run fully AI-free rounds for other roles or teams.

What do interviewers actually grade?

Not whether you used AI, but how. The Hello Interview write-up lists Meta's criteria as problem solving, code quality, verification and communication, and quotes an internal summary: "Should use AI, but need to show you understand the code."

The top answer in the Atlassian thread above was a process, not a trick: clarify, plan, implement in small steps, and review the AI's output for correctness. Other replies said evaluators check that you are "not blindly trusting" the AI.

Put together, the signals look like this:

Clarify the task and read the existing code before you prompt anything.
Plan out loud: what you will change, in which files, and why.
Implement in small steps. Ask for one function or one change at a time, not the whole feature.
Review every suggestion. Read it line by line and check it against the data models.
Test it yourself with normal cases, edge cases and the larger inputs.
Narrate what you are doing and why, including when you reject the AI's suggestion.

Where candidates say they struggle

From the candidate reports collected in the Hello Interview piece:

Orientation eats the clock. Around 5 to 6 minutes go on orientation, and candidates who skipped reading the codebase lost more time later debugging problems the data models would have explained.
Over-reliance. One candidate's feedback said they "appeared to rely heavily on AI, which impacted the quality of their solution."
The AI is less helpful than in practice. Several said it was weaker in the real round than when they practised with their own tools. One said it "gave wrong answers repeatedly."
Narrating while prompting is hard. One candidate found balancing the assistant and the interviewer harder than the coding itself.
Rules vary by interviewer, for example whether you may use AI in the bug-fix phase.

A practice routine you can run on any problem

You don't need a company's exact environment to practise the skill. Pick any medium problem, or better, a small project with two or three files, and run this:

Set a timer for 60 minutes. Leave the first 5 minutes for reading only. No prompts.
Write a one-line plan before your first prompt, then say it out loud: the approach, the complexity you expect, and which part you will hand to the assistant.
Prompt for one small piece at a time. Give the AI the relevant types and constraints. "Write the whole solution" is the prompt that gets candidates marked down.
Read every suggestion before you use it. Ask yourself: does this match the data model? What happens on empty input, duplicates, very large input?
Find one thing to change. If you can't find anything to question in a suggestion, you probably haven't read it closely enough.
Test before you trust. Run the examples, then write two edge cases of your own, then try a large input to check your complexity claim.
Narrate throughout. Record yourself or practise with a friend. You are aiming for steady narration while you prompt, not long silences followed by a summary.
Do the bug-fix phase without AI at least some of the time, since some interviewers restrict it.
Review afterwards. Note where the AI was wrong, where you accepted something without checking, and where you lost time.

If narration is the weak spot, our guide to thinking out loud in coding interviews has examples you can copy.

How to practise this on thita

thita has a practice mode built for this skill:

Open any DSA problem from /problems, or start with 3Sum in practice mode.
Switch on AI-allowed round in the bar above the editor.
Work the problem the way you would in the real round: ask the coach on any line as you would the round's AI assistant, and write, run and test the code yourself. It answers what you ask at the size you ask, won't hand over the whole solution, and is sometimes wrong on purpose, so check what it gives you.
When you finish, press Score my process. The coach grades the session on five things: clarifying and planning, scoped prompts, verification, building incrementally, and narration, with evidence from what you actually wrote and whether you caught its mistakes.

Two honest limits. First, thita does not simulate Meta's, Google's or anyone else's interview environment, codebase or model list. It trains the habits these rounds reward. Second, this is practice before an interview. It is not a copilot for use during one.

A note on honest practice

Real-time cheating tools that feed answers during interviews exist and are openly marketed. Companies have responded by flagging suspicious interviews and moving some rounds back in person. In 2025, Google CEO Sundar Pichai said engineering candidates would have at least one in-person interview, citing AI-assisted cheating. Reports differ on when and where he first said it.

This guide is only about rounds where the company provides the AI and tells you to use it. If a round doesn't say AI is allowed, assume it isn't, and ask. For more on where the line sits, see AI pair programming practice vs tools built for cheating.

Frequently asked questions

Do I have to use the AI in an AI-allowed round?

Reports suggest it is expected rather than strictly required, but candidates who avoid it entirely may run out of time on the implementation phase. Ask your recruiter how your round is scored.

Which AI model should I practise with?

Meta's round reportedly offers several. Google's pilot reportedly uses Gemini only. The skill that carries over is reviewing and testing output, not knowing one model's quirks.

Is LeetCode still worth doing?

Yes. Meta reportedly keeps a classic algorithm round alongside the AI one, and you can't review an AI's algorithm if you couldn't have written it yourself.

Open a problem and try AI-allowed round practice.

AI-Allowed Coding Interview Rounds: How They Work and How to Practise