AI-Assisted Coding Interviews: What to Expect and How to Prepare

Some companies now run coding rounds where candidates may, or are expected to, use an AI assistant. The documentation is uneven. Canva has published how it runs its version, Google and Meta are described mainly through press reporting and candidate accounts, and several other companies say to assume no AI unless you are told otherwise. This guide separates what companies have said from what candidates report, walks through the formats that have been described, lists the skills the sources say are evaluated, shows how to work with an assistant during the round, and ends with a preparation routine. Policies change quickly, so treat the company details as a snapshot from October 2026 and confirm the rules for your own loop with your recruiter. For the wider picture of each company's loop, see GreatFrontEnd's frontend interview process at Google, Amazon and Meta. This guide goes further on the frontend layer.

Which companies are using AI-assisted coding interviews?

Separate what companies have said from what candidates report, because most of the detail falls in the second group.

Canva is the best documented, because the company wrote the posts itself. For Meta, the version of its Full Loop Interview Guide linked from its careers site on 23 October 2026 asks candidates not to use unauthorized outside assistance, including AI tools like ChatGPT. So which Meta round allows an assistant is something your recruiter has to confirm, and the round mechanics in the next section are candidate reports, not Meta documentation.

Companies that say no AI unless you are told otherwise

Companies that allow an assistant in one round can forbid it in another, and several state the default in writing.

• Anthropic says candidates should not use AI during live interviews unless Anthropic indicates otherwise, and should complete take-home assessments without Claude unless told it is allowed. The page was last updated on 10 July 2025 on Anthropic's candidate guidance.
• Microsoft says that during assessments and interviews candidates should demonstrate their own skills without outside assistance unless explicitly permitted, per its How we hire page.
• OpenAI says expectations for AI and other tools vary by interview: some formats intentionally allow them and others assess independent problem solving without them. It says the allowed tools are explained in your preparation materials, and to ask your recruiter if unsure, in its interview guide.
• Amazon: Business Insider reported in February 2025 on internal recruiter guidelines that tell candidates not to use generative AI tools in interviews unless explicitly permitted, and that breaking the rule may lead to disqualification. That is an internal document as reported, not a public policy page, and it is from before the 2026 changes at other companies, so check it with your recruiter (Business Insider).

How do you find out whether you may use AI?

Do not assume. Ask in writing before the interview and keep the answer. Questions worth asking:

1. Is an AI assistant allowed in each round, and is it required in any?
2. Do I bring my own editor and tools, or is the environment provided? Is there a practice link?
3. Which languages and frameworks are supported?
4. Are there parts of the session, such as an initial bug-fixing phase, where AI is not allowed?

If the answer is still unclear when the session starts, ask the interviewer before you open the assistant, and say what you plan to use it for. Asking costs a few seconds, and the Amazon guidance above shows what using a tool where it was not permitted may lead to. Hello Interview's Meta guide reports that in the bug-fixing phase some interviewers explicitly say no AI while others leave it open, which is another reason to ask at the start of each phase.

What actually happens during an AI-assisted interview?

The formats that have been documented or reported are different interview formats, not one universal process.

Canva's later post, AI Interview Success, names three approaches that do not shine: an "AI showcase" that loses sight of the task, a "feature marathon" that accepts output quickly, and a "hands-off" style that delegates too much.

Two things stand out from the candidate reports. First, the problems that candidates describe for Meta, LinkedIn and Shopify are mostly language-agnostic (mazes, caches, intervals, robots on a grid), and Canva describes its tasks only as ambiguous, realistic product challenges. So a frontend candidate may be asked to judge generated code in a language or domain that is not React, and the habit being scored carries across. Second, the mechanics differ by round: Hello Interview reports that Meta's AI-enabled round sits alongside a traditional coding interview, with some interviewers asking for no AI in the first phase. GreatFrontEnd's company guides cover each loop in more detail: Canva, Shopify and LinkedIn.

What skills do interviewers evaluate?

The sources that describe these rounds use different words, but they point at the same few skills. None of them publishes a standard scoring rubric. Canva's list of five questions and the line quoted from Google's internal document are the closest published statements, and Meta's four areas come from candidate reports, so each skill below is tied to the evidence for it.

Candidate reports about Meta's round, collected by Hello Interview, describe four areas in total: problem solving, code quality, verification and communication. The wording differs between sources, and the thread is the same: directing the tool, reading what it produces, checking it, and explaining your decisions.

How should you work with an AI assistant during the interview?

This is the part you can practise. The sections below follow the order of the round: decide what to hand over, ask for something small, review what comes back, test it, explain it, and recover when the assistant is wrong. The code examples are practice material, not reported interview questions.

Decide what to delegate

Canva's interviewer guide describes using AI for skeleton code, research and routine work while you keep ownership of the result. Think of the assistant as something that drafts and you as the person who decides. The three approaches Canva says do not shine are the ones to avoid: showing off prompting technique, racing through features, and handing the whole task to the tool.

Open with the requirements, then write a bounded prompt

Canva's engineering post says it redesigned its questions to be ambiguous and realistic because a single prompt cannot solve them, and its interviewer guidance describes strong candidates asking about scope, clarifying assumptions and sketching an approach before implementing. So the first minutes should go to the requirements, not the assistant.

Suppose the prompt is: "Build a user picker for a team settings page. You type a name and choose a person." Before generating anything, turn the ambiguity into questions for the interviewer:

• Where does the data come from, and is it searched on the server or filtered on the client?
• What should the user see while loading, with no results, and after a failed request?
• Is selection single or multiple, and what does the parent component receive?
• What keyboard and screen reader behavior is expected? A type-ahead list is commonly built as an ARIA combobox, which has a specific keyboard model.
• Is there a minimum query length or a result limit?

Then agree on a small, demonstrable slice, for example "typing filters a list, arrow keys move, Enter selects, Escape closes", and say you will add the network layer second. Now the first prompt can be narrow. Compare a vague prompt with a bounded one:

Vague:
Build a user picker in React.

Bounded:
Context: React and TypeScript. api.ts exports
searchUsers(query: string, signal: AbortSignal): Promise<User[]>.
Task: write only the hook useUserSearch(query) that calls searchUsers and
returns { users, status } where status is 'idle' | 'loading' | 'error' | 'ready'.
Constraints: older requests must not overwrite newer ones, a failed request
must set status to 'error', and no new dependencies.
Do not write the component yet.

The bounded prompt gives you something short enough to review line by line, and it names the failure modes you already know about. Say the plan out loud: "I am asking for the hook only, because I want to check the async handling before it touches the UI." That one sentence shows the planning and the control that Canva's interviewer guide describes. It also avoids the "feature marathon" pattern, where speed of accepting output replaces judgment.

Review the suggestion before you accept it

Read what comes back as if a colleague had opened the pull request. Here is a version an assistant could plausibly give for the search results list:

import { useEffect, useState } from 'react';

export function SearchResults({ query }: { query: string }) {
  const [results, setResults] = useState<string[]>([]);

  useEffect(() => {
    fetch(`/api/search?q=${encodeURIComponent(query)}`)
      .then((res) => res.json())
      .then((data) => setResults(data.items));
  }, [query]);

  return (
    <ul>
      {results.map((item) => (
        <li key={item}>{item}</li>
      ))}
    </ul>
  );
}

It works when responses arrive in order. The React documentation describes the failure: if the user types "hello" quickly, the query changes through "h", "he", "hel", "hell" and "hello", each starting a fetch, and there is no guarantee about the order of the responses. If the "hell" response arrives after the "hello" response, setResults is called last with the wrong data. React's You Might Not Need an Effect calls this a race condition.

The documented fix is a cleanup function that marks the older effect's response as stale:

useEffect(() => {
  let ignore = false;

  fetch(`/api/search?q=${encodeURIComponent(query)}`)
    .then((res) => res.json())
    .then((data) => {
      if (!ignore) setResults(data.items);
    });

  return () => {
    ignore = true;
  };
}, [query]);

When query changes, React runs the previous effect's cleanup first, so the older closure sees ignore === true and skips setResults. You can also cancel the request itself with an AbortController: pass signal to fetch and call controller.abort() in the cleanup. Per MDN, an aborted fetch rejects with an AbortError, so your catch has to ignore that error name or you will report a failure the user never caused. Debouncing the input reduces the number of requests but does not by itself order the responses. GreatFrontEnd's React hooks interview questions cover the effect lifecycle that this fix depends on.

The same snippet has a second problem that the compiler will not report. MDN says a fetch() promise only rejects when the request itself fails, and does not reject when the server answers with an error status such as 404 or 504, so a then handler has to check Response.ok or Response.status. The same applies to a type assertion on res.json(): the TypeScript handbook states that type assertions are removed by the compiler and that there is no runtime checking (Everyday Types). GreatFrontEnd's TypeScript for React developers guide covers typing API data in more depth.

Other patterns worth a second look when an assistant produces frontend code are below. These are general review habits drawn from the linked documentation, not questions that any company has been reported to ask.

Run a test that can fail

An assistant will often offer a test with its code. Check whether it can fail for the bug you care about. Here is one an assistant could plausibly write for the search component above:

import { render, screen } from '@testing-library/react';
import { expect, it, vi } from 'vitest';
import { SearchResults } from './SearchResults';

it('renders results', async () => {
  vi.stubGlobal(
    'fetch',
    vi.fn().mockResolvedValue({ json: async () => ({ items: ['hello world'] }) }),
  );
  render(<SearchResults query="hello" />);
  expect(await screen.findByText('hello world')).toBeInTheDocument();
  expect(fetch).toHaveBeenCalledTimes(1);
});

(toBeInTheDocument comes from @testing-library/jest-dom.) It is green, and it proves very little. It passes against the racy component, because there is only one request and it resolves immediately. The mock is a plain object with a json method and no ok or status, so it is not shaped like a real Response. Here is the component with the stale-response fix and a status check combined:

export function SearchResults({ query }: { query: string }) {
  const [results, setResults] = useState<string[]>([]);
  const [error, setError] = useState<string | null>(null);

  useEffect(() => {
    let ignore = false;

    fetch(`/api/search?q=${encodeURIComponent(query)}`)
      .then((res) => {
        if (!res.ok) throw new Error(`Request failed with status ${res.status}`);
        return res.json();
      })
      .then((data) => {
        if (!ignore) {
          setResults(data.items);
          setError(null);
        }
      })
      .catch((err: Error) => {
        if (!ignore) setError(err.message);
      });

    return () => {
      ignore = true;
    };
  }, [query]);

  if (error) return <p role="alert">{error}</p>;
  return (
    <ul>
      {results.map((item) => (
        <li key={item}>{item}</li>
      ))}
    </ul>
  );
}

Run the assistant's test against this version and it fails, because the component checks res.ok and the mock does not provide it. A test that fails on the corrected code and passes on the buggy code is coupled to the implementation, not to behavior.

React Testing Library's guiding principles say the more your tests resemble the way your software is used, the more confidence they can give. Start from the bug you found and write the test that would have caught it. Control the order of the responses yourself, and use real Response objects (Response.json() is a static helper):

import { act, render, screen } from '@testing-library/react';
import { expect, it, vi } from 'vitest';
import { SearchResults } from './SearchResults';

function deferred<T>() {
  let resolve!: (value: T) => void;
  const promise = new Promise<T>((r) => (resolve = r));
  return { promise, resolve };
}

it('ignores a slow response for an older query', async () => {
  const slow = deferred<Response>();
  const fast = deferred<Response>();
  vi.stubGlobal(
    'fetch',
    vi.fn().mockReturnValueOnce(slow.promise).mockReturnValueOnce(fast.promise),
  );

  const { rerender } = render(<SearchResults query="hell" />);
  rerender(<SearchResults query="hello" />);

  fast.resolve(Response.json({ items: ['hello world'] }));
  expect(await screen.findByText('hello world')).toBeInTheDocument();

  await act(async () => {
    slow.resolve(Response.json({ items: ['hell on earth'] }));
    await new Promise((r) => setTimeout(r, 0));
  });
  expect(screen.queryByText('hell on earth')).not.toBeInTheDocument();
});

it('shows an alert when the server returns 500', async () => {
  vi.stubGlobal('fetch', vi.fn().mockResolvedValue(new Response('oops', { status: 500 })));
  render(<SearchResults query="hello" />);
  expect(await screen.findByRole('alert')).toHaveTextContent('status 500');
});

Against the original component the first test fails, because the late "hell" response replaces the "hello" results, and the second fails because nothing renders an alert. Against the combined component above both pass. That is the proof you want to be able to describe: the test fails for the right reason before the fix and passes after. Also point out findByRole('alert'): Testing Library's query guidance recommends getByRole-style queries first because they reflect how assistive technology users experience the page. GreatFrontEnd's senior testing interview questions go further on test design.

Explain your choices without leaning on the assistant

The discussion phase in Canva's interviewer guide is where strong candidates can explain every technical decision, discuss improvements and weigh trade-offs. Canva's guide also tells candidates that if the interviewer asks a question, they should say what they think before getting help from AI. So the first move is to answer from your own understanding, and use the assistant only for the part you want to confirm.

A useful answer has three parts: what the code does, what it assumes, and how you checked it. Suppose the interviewer points at the ignore flag and asks why it is there.

Two habits make the discussion easier. Narrate before you run: "I expect this to fail on the 500 case because there is no status check", then confirm or correct that expectation out loud, which is the advice in GreatFrontEnd's process guide for the silence problem. And keep a short list of things you would improve with more time, such as error telemetry, retries or an abort signal, because Canva's interviewer guide says strong candidates can name areas that would need more work in production.

What if the assistant is wrong or unhelpful?

Some candidates in Hello Interview's Meta guide report that the assistant was less helpful in the interview than in practice. Whatever the cause, plan to be able to write and debug the code yourself, and say what you are doing: "this suggestion is wrong because it ignores the status check, so I will write that part." When you do not understand a piece of generated code, say so. Hello Interview's LinkedIn guide reports a candidate who told the interviewer she preferred to write a response herself because she could not fully follow the suggestion, and who received positive feedback for it. That is a report from one source and not a rule, though it matches Canva's emphasis on owning every line you submit.

Common mistakes in an AI-assisted coding interview

• Assuming AI is allowed. Anthropic, Microsoft and OpenAI all publish language that makes permission explicit.
• Prompting for the whole solution. Canva describes this as the "hands-off" pattern. A bounded prompt keeps the output reviewable.
• Trusting a green test the assistant wrote. Check whether it can fail for the bug you care about.
• Accepting a lint suppression. An eslint-disable comment in generated code is a question to answer, not a style choice.
• Casting instead of validating. An as assertion on res.json() hides a missing status check and a wrong shape.
• Reviewing only on a desktop mouse. Tab through the page and check the layout on a phone.
• Fixing a race with debounce alone. Debounce reduces requests but does not order the responses.
• Going quiet while the assistant generates. Hello Interview's Meta report lists communication as one of four areas, and silence gives the interviewer nothing to assess.

How do you prepare for an AI-assisted coding interview?

Build a repeatable routine, and make most of it independent of any one assistant.

1. Get the rules in writing. Use the four questions above with your recruiter, including whether any phase restricts AI and whether the environment and a practice link are provided.
2. Work in an unfamiliar codebase. Open a small project you did not write, read it before you prompt, and write down what you think it does.
3. Clarify requirements first. Take an ambiguous prompt, write the questions you would ask the interviewer, and agree on a small slice before generating anything.
4. Use a permitted assistant with bounded prompts. Canva's interviewer guide recommends having your AI-integrated editor ready and a basic project structure prepared. Ask for something small, say what you expect, run it, and explain the result aloud.
5. Verify every change. Rebuild a buggy component in a scratch project, such as the search results list above, and write down the test that fails before the fix.
6. Explain your decisions. Practise answering "why did you accept that?" in two sentences before you touch the assistant.
7. Keep the fundamentals sharp without the tool. Some rounds do not allow one. GreatFrontEnd's UI coding questions and machine coding round guide are the no-assistant baseline, and the frontend debugging interview questions cover the DevTools side of finding problems.
8. Read the company's own page. Canva's two engineering posts, Anthropic's guidance, OpenAI's interview guide and Microsoft's hiring page are linked above. For the wider process at Google, Amazon and Meta, use the process guide.

Frequently asked questions

Is an AI-assisted coding interview easier because the assistant writes code? Not according to the sources. Canva says it found that AI assistants can trivially solve traditional coding interview questions, so it moved to more complex, ambiguous tasks, and it still expects you to take full ownership of any code you produce. Hello Interview reports that Meta candidates describe the implementation phase as harder than a medium LeetCode problem.

Which assistant or editor should I use? It depends on the company. Canva's interviewer guide suggests having your preferred AI-integrated editor and a basic project structure ready. In Hello Interview's Meta report, the assistant and models are provided inside the interview environment and recruiters can share a practice environment. Ask your recruiter which applies.

Do I still need to practise without an assistant? Yes. Some rounds do not allow one, and Hello Interview reports that Meta's AI-enabled round sits alongside a traditional coding interview, with some interviewers asking for no AI in the first phase.

Is there a standard scoring rubric? None of the sources I read publishes one. Canva lists five questions it evaluates, Google's internal document is quoted by Business Insider, and Meta's four areas are candidate reports.

Conclusion

The AI-assisted coding interview that frontend candidates face is documented unevenly: Canva has published its approach, Google and Meta are described mainly through reporting and candidate accounts, Shopify's Head of Engineering has spoken about it, and several other companies say to assume no AI unless told otherwise. What the sources share is a bar centered on what you do with the output: ask about requirements first, give the tool bounded work, read what comes back, check it with a test that can actually fail, and explain it as your own code. Get the rules in writing, practise in a codebase you did not write, and keep the fundamentals sharp for the rounds that do not allow an assistant.