The AI-Assisted Coding Interview at Meta, Google, and LinkedIn: What Changes and How to Prepare

The AI-assisted coding interview replaces the blank editor and the algorithm question with three things. You get a small existing codebase, an AI assistant in a chat panel, and three tasks, each harder than the last. The round grades judgment, not typing speed.

Judgment here means four actions. You divide the work into pieces, direct the assistant at one piece at a time, check what it returns, and explain what you are doing. A loop is the full set of interviews a company runs for one candidate, and the classic no-AI round is still in the loop.

As of this writing, Meta, Google, and LinkedIn each run a version, and they differ in the environment, the assistant, and what is graded. Your recruiter's email describes your own loop and overrides anything written here.

This guide describes the round and compares the three companies in one table. It then explains the approach that fails, the approach that works, and five preparation drills.

What the AI-assisted coding round is

A codebase is the set of source files that make up a project. The classic coding round gives you an empty editor and one algorithm question. The AI-assisted round gives you a small project you have never seen, with several files and existing logic.

Next to the editor is a chat panel with an AI assistant. The assistant can read the project files, so it can answer questions about them and draft code.

It cannot edit the files. Every change to the code is typed or pasted by you, and you are responsible for each line.

A prompt is the instruction you type to the assistant. This guide calls it an instruction or a request from here on.

The three phases of the round

All three companies use the same sequence of tasks. First you read the code and fix a bug. Then you add a feature to the working code, and then you make the code handle larger inputs or more edge cases.

An edge case is an input at the boundary of what the code must handle, like an empty list or a single item. The first phase tests reading. The second tests adding to code you did not write, and the third tests whether you can reason about cost and failure.

Meta, Google, and LinkedIn compared

Meta. One of the onsite coding rounds is now AI-enabled. You work in a multi-file project with a chat assistant that reads the files but cannot edit them.

You choose from a set of current frontier models. A frontier model is one of the newest and most capable models from the main AI labs.

Meta grades this round on the same four areas as its classic round: problem solving, code quality, verification, and communication. How well you write instructions to the assistant is not one of the four. The other coding round in the loop is still the classic algorithm round with no AI.

Google. Google runs a pilot called a code comprehension round for junior and mid-level roles on some US teams. The screen has three panels: a file explorer, an editor, and a chat with Gemini, Google's AI assistant.

The session is about 60 minutes, and classic algorithm rounds stay in the loop. Google is the one company of the three that grades AI fluency directly.

Interviewers check how clearly you specify what you want and whether you review generated code critically. They also check whether you can find and fix a suggestion that is close but wrong.

LinkedIn. LinkedIn runs the round in CoderPad, a shared online code editor built for interviews. The editor is in the center, the assistant panel is on the right, and some candidates report a file explorer too.

You choose from a small set of models. Candidates commonly report Claude, the assistant made by Anthropic, among them.

Using the assistant is optional at LinkedIn, and how often you use it is not graded. What is graded is whether you understand the code, think critically, and make sound decisions. Shopify, Canva, Rippling, and Stripe run similar formats.

The approach that fails: paste, accept, run, hope

You paste the whole task into the chat, accept the output, run it, and hope the tests pass. It feels fast, and it gives the interviewer nothing to observe.

Take Meta's four areas in order. Problem solving is absent, because the assistant chose the approach and you did not. Code quality is absent, because you cannot defend code you have not read.

Verification is absent, because you ran the code but never checked it against a case you chose. Communication is absent, because you stopped talking while you read the chat window. The interviewer watched a model solve the task and a person wait.

At Google the same behavior fails for a second reason. The pasted task is itself a vague request, and request quality is graded there. At LinkedIn it fails because accepting output without reading it is the opposite of critical thinking.

The approach that works: decompose, direct, verify, narrate

The approach that works has four steps, repeated for each piece of the task.

Decompose. Divide the task into small bounded pieces before you type anything into the chat. For a bug fix, that means naming the suspect function and the input that triggers the failure.

For a feature, it means listing the two or three changes it needs, in order.

Direct. Give the assistant one piece at a time, with the inputs, the expected output, and any limits.

"Write a function that returns the largest sum of any window of size k in this list" is a bounded request. "Fix the failing test" is not.

Here is the kind of draft an assistant returns for that request. It is close but wrong.

def max_window_sum(nums, k):
    best = 0
    window = sum(nums[:k])
    for i in range(k, len(nums)):
        window += nums[i] - nums[i - k]
        best = max(best, window)
    return best

Verify. Read every line before it goes into the project. State one edge case, trace the code by hand on it, and check the running time against the approach you intended.

The loop begins at index k, so the first window is never compared with best. For the list [5, 1, 1, 1] and k = 2 the answer should be 6, and this code returns 2. Setting best to window before the loop fixes it, and the case where k is larger than the list still needs a decision.

Narrate. Say what you are checking while you check it. For the draft above, two spoken sentences are enough.

"This loop starts at k, so it never compares the first window. I will set best to the first window before the loop." Those two sentences show problem solving, verification, and communication at the same time.

That check takes about 30 seconds. It is the whole difference between the losing approach and the winning one.

Why coding patterns matter more, not less

A coding pattern is a reusable solution shape, like sliding window or two pointers, that solves a family of questions. The classic round tests whether you can recognize a pattern and write it. The AI-assisted round tests whether you can recognize a pattern in code you did not write.

You cannot judge generated code against a pattern you do not recognize. If the assistant returns two nested loops where a sliding window would work, you need to see that in seconds. The pattern base is the same skill, tested a second way.

Grokking the Coding Interview teaches the patterns used by most interview questions, with runnable solutions for each one. The full list of patterns, with the signal that identifies each, is in LeetCode coding patterns.

How to prepare: five drills

1. Keep the pattern base current. The classic round still exists in the loop, and it has no assistant. Keep solving pattern problems under a time limit, and name the pattern before you code. How many problems is enough is covered in how much coding interview practice is enough.

2. Read an unfamiliar repository with a timer running. Once a week, open a mid-sized project you have never seen. Give yourself ten minutes to find the entry point, the data flow, and where the state is stored, then say the summary out loud. The AI round begins with this task.

3. Pair with an assistant under interview rules. Solve real tasks with an assistant under three rules. The assistant never sees the whole task, only the pieces you define. Nothing goes into the project until you have read and tested it, and you say each decision out loud.

4. Make verification a habit. Before you accept any generated function, do three things. State the edge case, trace the code on it, and check the complexity. Complexity is how the running time grows with the input size, and it is the most common defect in a close-but-wrong draft.

5. Rehearse narrating while you read generated code. Run two mock sessions where you think aloud while reading and judging the assistant's output. Silence while you read the chat is the most common reason an interviewer sees no communication at all.

What to ask the recruiter

Formats change and pilots expand, so ask three questions before the loop is scheduled.

• Which round is AI-enabled, and which rounds are the classic no-AI format?
• Which assistant or set of models does the environment provide?
• Is using the assistant optional, expected, or graded?

The answers change your preparation. If the assistant is Gemini, practice with Gemini. If use is optional, decide in advance which work you will give the assistant.

The general rules on AI use in interviews are in can you use AI in a coding interview.

Frequently asked questions

Can you use AI in a coding interview at Meta, Google, or LinkedIn? Yes, in the one designated round where the assistant is built into the environment. The other coding rounds in the loop are classic and have no AI. Ask your recruiter which round is which.

Is writing prompts graded in the AI-assisted coding interview? Not at Meta, where the graded areas are problem solving, code quality, verification, and communication. Google's pilot does grade how clearly you specify what you want and how critically you review the result. LinkedIn does not grade how often you use the assistant, only your understanding and your decisions.

Do you still need to practice algorithm questions for an AI-assisted interview? Yes. The classic no-AI round still exists in the same loop at Meta and Google. The AI round also tests the same pattern knowledge, because you cannot judge generated code against a pattern you do not recognize.

What does the AI assistant do during the interview? It reads the project files, answers questions about them, and drafts code in the chat panel. It cannot edit the files. You type or paste every change yourself.

How long is the AI-assisted coding interview? About 60 minutes at Google, and 60 to 90 minutes in reports from Meta. LinkedIn's length is not widely reported. Your recruiter's email gives the exact time for your loop.

Which companies run an AI-assisted coding interview? Meta, Google, and LinkedIn each run a version today. Shopify, Canva, Rippling, and Stripe run similar formats.

Company-specific practice covers the classic round in the same loop. For Meta, Grokking the Meta Coding Interview practices coding questions in the style Meta asks in that round. Google and LinkedIn have their own versions: Grokking the Google Coding Interview and Grokking the LinkedIn Coding Interview.

Related reading

• AI Company Interview Process: How ElevenLabs, Decagon, and Others Hire
• Anthropic Interview Process: Rounds, Questions, and How to Prepare
• Can You Use AI in a Coding Interview?
• What Do AI Interviews Look For?
• Coding Interview Cheat Sheet
• How Much Coding Interview Practice Is Enough?