Meta’s AI-Enabled Coding Interview (2025/2026): Complete Preparation Guide

Estimated read time: 15–20 minutes
Updated: October 2025

Meta recently introduced an AI-enabled coding round, and this has caught quite a number of people off guard. Previously these rounds were straightforward, 2 LeetCode-style algorithm questions that you'd typically find verbatim (or variants) on the Meta-tagged LeetCode list, but everything has changed. The aim of this guide is to give you as much insight as possible to help you maximize your chances of a positive outcome.

Quick Reference: What You Need to Know Now

What this round is (and isn't):

This is not an interview about how well you use AI. The AI is more of a tool to help you demonstrate your coding skills more efficiently and in a more job-relevant way. You're being evaluated on problem-solving, code quality, and verification, not prompt engineering.

The format:

60 minutes in CoderPad with an AI-assist chat window. You write, run, and debug real code. The assistant helps with scaffolding and boilerplate but as of the time of writing, none of the AI models are reasoning models. Expect it to occasionally hallucinate, suggest suboptimal approaches, or miss edge cases. You will most likely receive a mini multi-file codebase and be asked to extend or debug it.

Question structure:

Expect one thematic question with multiple parts, stages, or checkpoints.

AI assistant models you can switch between:

GPT-4o mini, Claude 3.5 Haiku, Llama 4 Maverick, and possibly more may be added in the future. The interface feels similar to VS Code Copilot. Get familiar with at least one of these models before your interview so you know their strengths, weaknesses, quirks, and limitations. The AI can see all code in your editor, so you don't need to copy-paste into the chat.

On using AI:

"Some candidates won't use the AI at all and perform amazingly, while others will use it a lot and also do well. Use it at whatever level you are comfortable with. You will still need to understand the underlying code, which helps with progressing through the interview."

That said, AI excels at boilerplate code, grunt work, and heavy lifting (loads of typing), so let it handle those tasks while you focus on design and correctness.

Who takes this:

Confirmed for software engineers (SWE) and ML engineers. Expected but unconfirmed for Production engineers in their SWE coding rounds.

When it started:

We began seeing candidates receive this round in early October 2025.

What interviewers evaluate:

Problem Solving: Are you able to clarify and refine problem statements? Can you generate solutions to open-ended and quantitative problems?

Code Development and Understanding: Are you able to navigate a codebase to develop and build on working code structures and to evaluate the quality of produced code? Can you analyze and improve code quality and maintainability? Does code work as intended after it is executed?

Verification and Debugging: Can you find and mitigate errors to ensure code runs / functions as intended? Are you able to verify solutions meet specified requirements?

Technical Communication: How well can you communicate reasoning, discuss technical ideas, ask thoughtful questions, and incorporate feedback?

Your highest-leverage moves:

Build a requirements checklist before touching code
Write tests first (or understand pre-written tests if provided)
Generate a skeleton before implementing logic
Pipeline your work: while AI drafts, you review or explain
Run and debug in small iterations; fix one thing at a time
Be ready for non-coding discussion: runtime analysis, trade-offs, contract changes, data reasoning

Language choice:

You still choose your preferred programming language.

Practice environment:

Ask your recruiter for the practice CoderPad session if you didn't receive one automatically. The practice CoderPad has an AI-assist tab with the AI model switcher and chat window.

Common Questions (Before We Go Deeper)

Can I run code during the interview?

Yes. Running and iterating is central to how you're evaluated. You're expected to execute, read failures, and fix bugs in real time.

Does the AI just solve it for me?

No. It's a helper, not a solver. Think of it as a brilliant assistant who can scaffold fast but needs your guidance on what to build and your review to catch mistakes.

How capable is the assistant?

Helpful for boilerplate and routine tasks, but not a frontier reasoning model. It can suggest suboptimal algorithms, miss constraints, or introduce subtle bugs. You're responsible for verification.

Which AI models are available in the interview?

As of this writing, you can switch among GPT-4o mini, Claude 3.5 Haiku, and Llama 4 Maverick inside CoderPad's AI panel; other models will likely be added in the future. The AI-assist window feels similar to VS Code Copilot but models differ in strengths (e.g., test-writing vs. refactors) and speed. Practice with at least one model in advance.

Will I work in a single file or a project?

You will likely get a mini project, e.g., for Python: multiple .py files plus a requirements.txt. Expect to read unfamiliar modules, extend them, and fix bugs. Comfort with navigating multi-file code is key.

How are tests set up in Python?

You may see unittest.TestCase classes with failing tests. Your task may be to trace failures to the right file/function and fix them without breaking other cases. Brush up on the unittest library basics. In some problems, tests are pre-written to save you time; in others, you'll write your own.

What's the question structure?

Expect one thematic question with multiple parts or checkpoints. Early checkpoints are achievable even if you use AI minimally. Later stages reward deeper engagement and may require more complex reasoning or code.

What kinds of problems appear?

You might debug an existing snippet, extend a half-built module, or implement a utility from scratch.

How do I prepare?

Practice with a similar-level AI (like GPT-4 or Claude in standard mode, not extended thinking). Drill the workflow: requirements → assertions → skeleton → implement in chunks → run → debug. Get comfortable reviewing and quickly understanding code that you did not write.

Why This Round Exists (And What It Means for You)

Meta isn't testing whether you can write perfect code from memory. In production, you'll work with AI tools, inherit unfamiliar codebases, and verify behavior with tests. This round simulates that reality.

The shift changes what "good" looks like:

Speed matters, but correctness and verification matter more.
Talking through every line wastes time; speak when it adds clarity.
Big, unreviewed pastes from AI are a red flag; small, verified iterations show control.

Important: Be prepared for non-coding parts of the question, such as runtime analysis, justifying trade-offs, making contract changes, or reasoning about data. These areas are where AI is less powerful and where you can demonstrate deeper understanding.

The Four Evaluation Lenses (What Interviewers Are Watching For)

1) Problem Solving

Official emphasis: Clarify and refine problem statements; generate solutions to open-ended and quantitative problems.

Before you write a single line, you need to show you understand the problem deeply. The following will help:

Restate the task in your own words, confirming inputs, outputs, and constraints
Break it into small steps: data flow → core operations → edge handling
Keep a visible checklist of crucial information or objectives that are easy to forget

When you do this well, you're building a shared mental model with your interviewer. You'll catch ambiguities early (like "Should the program be case-sensitive? Should the result be in descending order?") and avoid rework later.

Takeaway: Start with clarity, not code. A two-minute planning phase saves you from preventable debugging later.

2) Code Development and Understanding

Official emphasis: Navigate a codebase, build on working structures, evaluate and improve code quality and maintainability; ensure code works as intended when executed.

Many problems give you a partial implementation: maybe a skeleton with TODOs, or a buggy function you need to fix. Strong candidates:

Map the structure quickly: which files matter, where's the entry point, what contracts exist
Extend without breaking: respect naming conventions, match the existing style
Choose appropriate structures: pick the right algorithm and data structure for the constraints

Python project format: You may get multiple files (e.g., main.py, utils.py) plus requirements.txt. Start by mapping the entry point, public interfaces, and module responsibilities. Confirm contracts before editing.

This is closer to real work than a blank-file problem. You're proving you can drop into an existing system and make it better.

Takeaway: Treat the starter code like a production codebase. Understand it (at least at a high level by skimming the interface) before you modify it.

3) Verification and Debugging

Official emphasis: Find and mitigate errors; verify that solutions meet the specified requirements.

Here's where many candidates lose points: they write code, glance at it, and assume it works. Interviewers want to see proof:

Outline your test cases and/or write tests
If tests are pre-written, read them carefully to understand requirements before modifying code
Cover the golden path plus key edges: empty input, large values, duplicates, negatives
When tests fail, read the output carefully and apply small, local fixes
Re-run the entire test suite after each fix to guard against regressions

Good candidates test as they go. They don't wait until the end to run code; they verify each piece as it's built.

4) Technical Communication

Official emphasis: Communicate reasoning, discuss technical ideas, ask thoughtful questions, and incorporate feedback.

You don't need to narrate every thought, but you do need to speak at key moments:

Upfront: What's your plan and why is it optimal for this timebox?
During work: While AI generates code, explain your next steps or trade-offs
After runs: Briefly summarize what passed, what failed, and what you'll fix
At the end: State complexity, acknowledge trade-offs, mention next steps if time allowed

Avoid two extremes: long silences (the interviewer can't follow your thinking) and constant talking (blocks your ability to think and review).

Takeaway: Speak when it adds clarity. Silence while you review code is fine; silence while you're lost isn't.

A Repeatable Framework (Works for Build, Extend, or Fix)

This six-step process works for any problem type. Adapt the depth of each step based on the task and which checkpoint you're tackling, but don't skip steps.

Step 1: Understand and Clarify

Start by restating the problem in one or two sentences. Confirm:

What are the inputs and outputs?
What are the constraints (time limits, memory, input size)?
Are there edge cases called out explicitly?
What does "correct" mean (exact match, approximate, any valid answer)?
What checkpoint are you working on (if the question has stages)?

Then build your requirements checklist, a visible list of must-pass behaviors. For example, if you're building a word-guessing game:

Accept a secret word and reveal blanks initially
Update display after each valid guess
Track remaining attempts
Handle invalid input (non-letters, repeated guesses)
Detect win/loss conditions

This checklist becomes your north star. Every test you write and every line of code should map back to an item here.

Step 2: Tests First (or Understand Pre-Written Tests)

If tests are pre-written (often the case to save time), you can read them carefully to extract intended behavior.

If tests aren't provided, convert your requirements checklist into simple assertions using the provided test framework:

# this is illustrative pseudocode, tests won't necessarily look like this
assert game.display() == "_ _ _ _ _"  # golden path: initial state
assert game.guess('e') == True        # correct letter
assert game.display() == "_ e _ _ _"  # display updates
assert game.guess('z') == False       # incorrect letter
assert game.remaining_attempts == 5   # attempts decrease
assert game.guess('E') == True        # case-insensitive
assert game.guess('e') == False       # duplicate rejected
assert game.is_won() == False         # game continues

Cover the golden path (the obvious happy case) and these standard edges:

Empty input (null, empty string, zero)
Large values (max constraints, performance boundaries)
Duplicates (repeated guesses, repeated words in a dictionary)
Negatives (invalid characters, out-of-range values)
and other relevant ones to your problem

You can ask the AI to propose additional test cases, but you curate; keep the list lean and relevant.

Step 3: Co-Design with AI (You Lead)

Now it's time to consider approaches. Share your initial idea with the AI and ask it to suggest one or two alternatives.

Example scenario: You're building a leaderboard feature for your word game. You need to display the top 3 players by score.

Your initial thought: "Sort the entire player list by score and take the first three."

But you remember performance constraints: the player list could have 100,000 entries. Sorting everything is O(n log n), and you're doing this on every game completion.

Your prompt to AI:

"I need the top 3 players by score from a list of up to 100,000 players. My first thought is to sort the entire list and take the first three. Can you suggest a more efficient approach and compare the trade-offs?"

AI might suggest:

Use a min-heap (priority queue) to maintain only the top 3 players

Ideally a good candidate should be coming up with optimal approaches, but if stuck AI is there to help.

Now you compare options on:

Simplicity (easier to implement correctly in 60 minutes)
Runtime (does it meet constraints?)
Memory (does it fit in memory limits?)
Amount of code (less code = fewer bugs)
Risk (is the approach well-understood?)

Pick the most optimal feasible approach.

Once you've chosen, briefly outline which files or functions you'll touch. This gives your interviewer a roadmap and keeps you organized.

Takeaway: The AI can brainstorm, but you decide. Choose the approach that balances optimal with achievable.

Step 4: Skeleton First

Before implementing any business logic, generate the structure:

Classes, interfaces, types, enums (define contracts)
Function stubs with clear names and TODO comments
File layout (if working with multiple files)

If you're extending existing code, match the naming and style exactly. Consistency shows you respect the codebase.

For a simple word-guessing game:

class WordGame:
def __init__(self, secret_word: str, max_attempts: int = 6):
    # TODO: Initialize game state
    pass

def guess(self, letter: str) -> bool:
    # TODO: Process guess and return True if correct
    pass

def display(self) -> str:
    # TODO: Return current word state with blanks
    pass

def is_won(self) -> bool:
    # TODO: Check if all letters guessed
    pass

def is_lost(self) -> bool:
    # TODO: Check if attempts exhausted
    pass

Here's why this matters: a skeleton lets you and the interviewer see the overall shape before details distract you. It also makes the next step (iterative implementation) much smoother.

Takeaway: Structure first, logic second. The skeleton is your blueprint.

Step 5: Iterative Implementation (Pipelining)

Here's where the AI becomes a force multiplier, if you use it correctly. The key is pipelining: work in parallel with the assistant instead of waiting idle.

The workflow:

Ask the AI to implement one small slice (one or two functions max)
While the AI drafts, you review the previous chunk line by line
Paste only after a quick correctness and style check
Run your assertions for that slice
If tests pass, move to the next slice; if they fail, debug before continuing

Example prompt:

"Implement only __init__ and display according to the skeleton. Store the secret word, track guessed letters as a set, and return the word with unguessed letters as underscores. No changes to other functions."

Why small slices? Large AI outputs compound errors. If the AI misunderstands a requirement, you catch it early and fix it fast. Small iterations keep you in control.

While the AI generates, you can:

Review the previous function for edge cases it missed
Explain your next steps to the interviewer
Tighten your assertions based on what you learned

This parallelization eliminates idle time. You're always either reviewing, explaining, or implementing.

Takeaway: Small requests, constant review, minimal idle time.

Step 6: Verify and Debug

Once a slice is implemented, run your assertions. Read failures carefully:

What's the actual vs. expected output?
What does the stack trace or error message tell you?
Which single function or line is the most likely culprit?

Apply the smallest possible fix; change one thing, re-run, verify. Avoid the temptation to refactor three functions at once. If you introduce a regression, you won't know which change caused it.

Example debug scenario:

Your test fails: assert game.guess('E') == True returns False.

You check the code:

def guess(self, letter: str) -> bool:
if letter in self.secret_word:
    self.guessed_letters.add(letter)
    return True
return False

The bug: case sensitivity. The secret word is lowercase, but the guess is uppercase. The fix:

def guess(self, letter: str) -> bool:
letter = letter.lower()  # Normalize input
if letter in self.secret_word:
    self.guessed_letters.add(letter)
    return True
return False

Re-run the entire test suite after each fix to guard against regressions. When a test passes, ensure previous tests still pass. Regressions are a silent killer in timed interviews.

If you're stuck, explain what you see to the interviewer. Often, verbalizing the problem reveals the solution. If not, they may offer a nudge.

Takeaway: Debug in tiny increments. One fix, one run, one verification. Always run all tests.

Using the AI Assistant Like a Pro

The assistant is a tool, not a teammate. Here's how to get the most out of it without letting it derail you.

Give Excellent Context

Every prompt should include:

What you're trying to do (bullet the requirements)
Constraints (time, memory, language version)
Current state (which file, which function, what's already working)
A tiny example (input → expected output)

Pro tip: The AI can see all code in your editor, so you don't need to copy-paste code into the chat. Just reference the file in the context or the function name in your prompt.

Example:

"I'm building a word-guessing game. Requirements: track guessed letters, reveal correct letters in display, handle case-insensitive input, reject duplicate guesses. Language: Python 3.10. Currently have the class skeleton. Example: secret word 'hello', after guessing 'e' and 'l', display shows '_ e l l _'."

Constrain Scope Tightly

Avoid asking the AI to "implement everything", unless you know what you're doing. Instead prefer something like:

"Implement only guess and display methods. No changes to __init__ or game state tracking."

Tight scope means:

Smaller output to review
Fewer places for errors to hide
Easier to roll back if something breaks

Offload the Right Work

The AI excels at:

Boilerplate (imports, class structure, docstrings)
Parsing (reading files, splitting strings, validating formats)
Variable naming (when you don't want to think about it)
Assertion scaffolds (generating test cases from your requirements table)
Heavy lifting (loads of typing, repetitive code patterns)

The AI struggles with:

Subtle correctness (off-by-one errors, boundary conditions)
Optimality (it may suggest O(n²) when O(n log n) is feasible)
Uncommon patterns (it defaults to common solutions even when they don't fit)
Deep reasoning (trade-off analysis, contract design, data structure choice)

Takeaway: Let the AI handle grunt work. You handle design, verification, and anything requiring judgment.

Stay Vigilant (The Model Isn't Frontier-Level)

The assistant can make mistakes:

Missing a constraint you mentioned
Suggesting a solution that works for small inputs but fails at scale
Introducing subtle off-by-one errors
Using the wrong library function or method signature

Your job: review every line before pasting. Look for:

Does it match the requirement?
Does it handle the edges you listed?
Is the logic sound (not just plausible)?

If something feels off, trust your instinct. Re-prompt with a precise correction:

"Your solution doesn't handle duplicate guesses correctly. Update guess to check if the letter is already in guessed_letters and return False immediately without decrementing attempts."

If the AI suggests something clearly wrong and you're uncertain, your interviewer may step in to redirect. Don't hesitate to ask clarifying questions.

Takeaway: The AI is helpful but fallible. Your judgment is the final check.

Avoid the One-Shot Mega-Prompt

It's tempting to write a giant prompt like "Implement the entire word game with these 10 requirements." Resist.

Why this fails:

Giant outputs are hard to review (you'll miss errors)
Errors compound (if step 2 is wrong, steps 3–10 are wrong too)
You lose control (the AI made design decisions you didn't approve)

Instead: break it into 3–5 small prompts. Review and verify each before moving on.

Takeaway: Small prompts, constant verification. Stay in the driver's seat.

What to Say (And When to Say It)

Communication isn't about talking constantly; it's about adding clarity at key moments.

At the Start (1-2 minutes)

"I'll focus on checkpoint 1 first: the core game loop. I'll handle the golden path, then these edges: empty input, invalid characters, duplicate guesses, and case sensitivity. I'm starting with the core game logic, then adding input validation."

This shows you have a plan and you've thought through edge cases.

Before You Code (30 seconds)

"I'll generate a skeleton first with class structure and method stubs, then implement in small chunks starting with the display logic."

This confirms your strategy and gives the interviewer a mental roadmap.

While the AI Generates (15-30 seconds)

Instead of sitting silent while the AI drafts, explain what's coming next:

"While it generates the guess handler, I'll review the display logic we just pasted. The tricky part will be handling repeated letters in the secret word; I want to ensure all occurrences are revealed."

This fills what would otherwise be dead air and shows you're thinking ahead.

After Running Tests (10-15 seconds)

"Golden path passed. The case-sensitivity test failed because we're not normalizing input. I'll add a .lower() call at the top of the guess method."

Brief, specific, and shows you understand the failure.

When Discussing Non-Coding Topics (30-60 seconds)

Be ready to discuss:

Runtime analysis: "The current approach is O(n log n) due to sorting. If we used a heap for top-k, it would be O(n log k)."
Trade-offs: "Sorting is simpler to implement and maintain, but the heap approach scales better for large k."
Contract changes: "If we changed get_top_players to return a stream instead of a list, we could process results incrementally."
Decision justification: e.g. why you introduced a cache

At the End (30 seconds)

"Time complexity is O(n) per guess where n is word length. Space is O(m) for tracking guessed letters where m is alphabet size, so O(1) effectively. If I had more time, I'd add a hint system and persist game state."

This shows you understand trade-offs and can prioritize.

Takeaway: Speak to clarify, not to fill silence. Quality over quantity. Expect non-coding discussion.

Seven Anti-Patterns That Quietly Sink Candidates

1) Letting AI Drive

You ask the AI to "solve the problem" and paste whatever it gives you without reviewing. This shows you're a passenger, not a driver.

Fix: Always propose your plan first. Use AI to execute your vision, not to decide your vision.

2) Giant Unreviewed Pastes

You prompt once, get 100 lines, paste it all, and hope it works. When it doesn't, you're lost.

Fix: Request small outputs (10–20 lines). Review line by line before pasting.

3) Skipping Tests

You eyeball the code and say "looks good" without running it. Then you're shocked when it fails on edge cases.

Fix: Write tests (or understand pre-written ones). Run early, run often.

4) Long Stretches of Silence

You go quiet for five minutes while you think or code. The interviewer has no idea if you're stuck or making progress.

Fix: Narrate your high-level plan or next step every 60–90 seconds. You don't need to explain every line, just enough so they can follow.

5) Nonstop Narration

Fix: Talk when it adds value. Silence while you review code is fine.

6) Premature Optimization

You spend ten minutes refactoring variable names or tweaking constants before proving the core logic works.

Fix: Correctness first. Optimize only after tests pass and only if time allows.

7) Ignoring Regressions

You fix one test and break two others. You don't notice because you only re-run the failing test.

Fix: Run all assertions after every fix. Regressions are silent killers.

How to Prepare (Actionable Steps)

1) Request the Practice CoderPad

If you didn't receive an official practice session, ask your recruiter. It's the best way to familiarize yourself with the environment and the AI model switcher.

2) Run AI-Assisted Mocks

Practice with a similar-level AI (GPT-4o mini, Claude 3.5 Haiku, or Claude in standard mode). Simulate the workflow:

Start with a problem you've never seen
Build a requirements checklist
Write assertions first (or understand pre-written tests)
Use AI to scaffold and implement in chunks
Review every line before pasting
Debug in small iterations

Practice three scenarios:

Building from scratch (implement a utility like a word game or data processor)
Extending unfamiliar code (add features to a starter codebase with multiple files)
Debugging (fix broken code under time pressure, especially multi-file projects)

If practicing with Python, work with projects that use unittest.TestCase so you're comfortable tracing test failures across files.

3) Edge Cases

Get comfortable with rigorously laying out test cases

4) Practice Pipelining

Set a timer and practice working while the AI generates:

AI drafts function X → you review function X-1 or explain next steps
This minimizes idle time and shows you can multitask effectively

5) Mock with a Human

Get feedback from someone else. If you can, record the session so you can play it back.

6) Practice Non-Coding Discussion

Spend time analyzing problems for:

Runtime complexity (best, average, worst case)
Trade-offs (speed vs. memory, simplicity vs. performance)
Alternative approaches and when each makes sense
Contract design (what should functions promise?)

Final Thoughts: You're Still the Engineer

The AI assistant changes your workflow, but it doesn't change what interviewers value: clear thinking, sound judgment, and the ability to verify your work.

The candidates who succeed treat the AI like a capable assistant: useful for speed, but needing oversight. They plan first, test constantly, and review everything before it ships.

The candidates who struggle treat the AI like a magic box: paste without review, skip verification, and hope for the best.

Meta introduced this format because it mirrors real engineering. You'll work with AI tools, inherit unfamiliar code, and verify correctness with tests. The interview is a 60-minute preview of that reality.

Remember: this is not an interview about how well you use AI. It's about demonstrating your coding skills efficiently and in a job-relevant way. Some candidates barely touch the AI and excel. Others use it heavily for boilerplate and grunt work while driving design themselves, and they also excel.

Best of luck, and remember: you're the engineer. The AI is just the tool.