AI-Enabled Coding Interviews 2026: How to Use AI the Right Way

An AI-enabled coding interview is a live round where the company hands you an approved AI assistant and grades how well you work with it. You still need to understand the problem, pick the right approach, and verify every line, because interviewers score your judgment, not the model's output. Meta, Canva, LinkedIn, and Karat all run versions of this format in 2026, and each one rewards the same workflow: plan first, prompt in small steps, test constantly, and explain as you go.

Key Takeaways

• Meta's AI-enabled round is 60 minutes in a multi-file CoderPad project with a model picker and an assistant that, per candidate reports, answers in chat but does not edit your files.
• Interviewers grade four things: problem solving, code quality, verification, and communication. "The AI solved it" earns nothing on its own.
• The winning workflow is plan, scope, prompt, read, run, explain. Candidates who paste the full problem into the chat and accept the first answer are the most common failures.
• AI is allowed only in the designated round. Other rounds in the same loop still ban it, and outside tools are usually off limits even in the AI round.
• Practice the format, not just the algorithms. Use a chat-only assistant on an unfamiliar repo with a 60-minute timer.

Which Companies Allow AI in Coding Interviews?

A small but growing group of companies now runs officially AI-allowed rounds. The table below covers the formats with public or candidate-reported detail. Treat it as a starting point and confirm the specifics with your recruiter, since these programs change often.

Canva has said on its engineering blog that it would rather watch candidates use AI openly than police it, and that it evaluates how engineers review, debug, and own AI-generated code. Karat describes its NextGen interviews as built around an integrated assistant, with external AI tools off limits.

Meta is the case most candidates ask about. According to the Meta interview process guide, the AI-enabled round was piloted from October 2025 and can replace one of the standard coding interviews. LinkedIn's loop is reported to follow a similar pattern on CoderPad. Cursor is a useful contrast: its early screens ban AI beyond autocomplete, and AI use comes later in its work trial.

Everywhere else, the default is still no AI. If you are unsure where the line sits, read is using AI during a coding interview cheating before your loop.

What Does the AI-Enabled Round Look Like?

The AI-enabled round is a longer, more realistic task than a classic algorithm question. Instead of two 20-minute problems, you get one extended problem inside a small codebase, with real code execution and existing tests.

Candidate reports of Meta's version, compiled by Hello Interview, describe a consistent shape:

• Layout: a three-panel CoderPad view with a file explorer, a code editor, and an AI chat panel next to the instructions.
• Models: a dropdown of several models from different providers. Reports differ on the exact list, so do not rely on one.
• Assistant limits: the model replies in chat only. You write or paste every change yourself.
• Languages: several mainstream languages, typically including Python and Java. Confirm yours with the recruiter.

The task often moves through three stages. First you fix a planted bug in the existing code, and some interviewers restrict AI use for this part. Then you implement the core feature, which candidates describe as a substantial chunk of code. Finally, larger test cases expose a slow solution and push you to optimize, sometimes by switching algorithms entirely.

CoderPad lets employers configure how the assistant behaves, so the chat-only setup is not guaranteed everywhere. Some pads may let the assistant edit code directly. Ask your recruiter which mode you will have so your practice matches.

What Do Interviewers Evaluate?

Interviewers grade your engineering judgment with the AI as a tool, not the AI's raw output. Candidate reports describe four areas for Meta, and Canva's public description of what it evaluates overlaps closely. The table adds a fifth, AI direction, which is how interviewers see the first four in action.

The summary that circulates among candidates is blunt: use the AI, but show you understand the code, explain the output, test before trusting it, and do not try to prompt your way out of a problem you do not understand.

Two details matter more than people expect. First, some candidates report that the interview assistant is less forthcoming than the same model at home, so do not count on it to spot bugs for you. Second, the bug-fix stage tests whether you can read code without the model, which is the same skill covered in the debugging interview guide.

A Prompting Workflow That Scores Well

The best prompting workflow for an AI-enabled coding interview has six steps, and the first two happen before you touch the chat panel. Run this loop for every piece of the task.

1. Read before you prompt. Spend the first five minutes on the file tree, the entry point, the data models, and the tests. Say out loud what each file does.
2. State your plan to the interviewer. Name the approach, the data structures, and the expected complexity. This is where problem-solving credit is earned.
3. Scope one piece. Pick a function or a single change, not the whole feature. Smaller prompts produce code you can actually review in 60 seconds.
4. Prompt with context and constraints. Paste the relevant types or function signatures, state the expected behavior, and say what not to change.
5. Read every line, then run the tests. Explain the generated code back in your own words before pasting. Run the tests immediately after each change.
6. Narrate the result. Say what passed, what failed, and what you will do next. If the output was wrong, say why.

Step 2 is the one most candidates skip under pressure. If the interviewer hears your plan first, every later prompt reads as you directing a tool. If they hear nothing until a 90-line answer appears, it reads as the model doing the thinking. The think out loud guide has phrasing you can borrow.

Sample Prompts That Score Well

Good prompts in an AI-assisted coding interview are short, specific, and show that you already know what you want. Here are templates for each stage of the round.

Understanding the codebase:

Here is graph.py and test_graph.py. In two or three sentences, explain
what build_adjacency does and what shape it returns. Do not suggest
changes yet.

Implementing a scoped piece:

Write a function shortest_path(grid, start, end) -> int using BFS.
grid is a list[list[str]] where '#' is a wall. Return -1 if unreachable.
Use collections.deque. Do not modify any other function. Keep it under
25 lines.

Generating tests:

List 6 edge cases for shortest_path that the current tests miss,
then write them as pytest functions. Include start == end and a
fully walled-in start cell.

Optimizing after a timeout:

The current solution is O(V * E) because it runs BFS from every node.
The large test has 10^5 nodes. Suggest one approach that brings this
under O(V + E) and explain the trade-off before writing any code.

Compare those with prompts that hurt your score: "solve this problem," "fix the bug," or pasting the whole instructions file with no comment. Those prompts hand the decision-making to the model, and decision-making is what the round measures.

How to Read and Test AI-Generated Code

Treat every response as a pull request from a fast junior engineer who never ran the code. Your job is to review it, not to admire it.

A quick review checklist you can run in under a minute:

• Signature match: does the function take and return exactly what the caller and tests expect?
• Boundaries: check empty input, single element, duplicates, and the first and last index.
• Hidden state: look for mutated arguments, shared default values, and globals.
• Complexity: find the nested loops and confirm they fit the input size.
• Invented APIs: confirm every library call actually exists in the language version you are using.

Here is a typical example of a confident but wrong answer. Asked for a function that returns the longest path length through a list of tasks, a model might produce this:

def visit(node, graph, seen=set()):
    if node in seen:
        return 0
    seen.add(node)
    return 1 + max((visit(n, graph) for n in graph[node]), default=0)

It passes a single small test, then fails on the second call because seen=set() is a shared default that persists between calls. It also uses seen as a visited set instead of a memo, so a node reached by a second route returns 0 and the longest path comes out too short. The fix is a fresh dictionary per top-level call that caches each node's longest path. Saying both of those things out loud, then fixing them, is worth more than the original code.

Run tests after every pasted change, not at the end. If something breaks, you know exactly which paste caused it. When you are stuck on a failure the AI cannot explain, fall back to the steps in what to do when you are stuck in a coding interview.

Common Mistakes in AI-Enabled Rounds

Most failures in AI-enabled rounds come from habits, not knowledge gaps. These are the patterns candidate reports and company rubrics point to most often.

1. Dumping the whole problem into the chat. It skips the problem-solving signal and usually produces code that does not fit the existing codebase.
2. Refusing to use the AI at all. The task is sized for AI help. Typing 150 lines by hand often means you never reach the optimization stage.
3. Going silent while prompting. The interviewer cannot grade thinking they cannot hear.
4. Trusting practice-environment behavior. The interview model may be restricted differently than the one you used at home.
5. Pasting code you cannot explain. If the interviewer asks "why this line?" and you do not know, that block of code now counts against you.
6. Skipping the existing tests. They tell you the expected behavior faster than the instructions do.
7. Using AI in the wrong round. The permission covers one interview. Platforms like CoderPad still log paste and focus events in the others, as covered in CoderPad cheating detection.

How to Set Up Practice for an AI-Assisted Coding Interview

The fastest way to prepare is to rebuild the format at home and run it until the workflow is automatic. Algorithms still matter, since the core and optimization stages lean on BFS, DFS, backtracking, tries, heaps, and dynamic programming. The coding interview patterns cheat sheet covers those templates.

A simple two-week practice setup:

For the chat-only constraint, open any assistant in a separate window and copy code by hand rather than using an in-editor agent. It feels slower, and that is the point: it approximates the reported Meta setup and forces you to read what you paste. Record at least one session and listen back for silent gaps longer than 30 seconds.

If your loop also includes standard rounds, keep doing timed algorithm practice in a no-run editor. The two formats reward different habits, and preparing for only one leaves you exposed in the other.