Meta’s new AI interview and how to prepare
How the round actually works
You work in CoderPad with three panels, file explorer, code editor, AI chat. The AI can only respond in chat! it can’t edit files directly, so you’re typing/pasting everything yourself. SO not its not like giving claude everything. You will work inside a multi file project with existing classes and data models already written TAKE TIME TO READ IT, and understanding it quickly, this is big part of the challenge.
The session runs through phases, bug fixing, core implementation, and optimization over about 60 minutes. You can switch between AI models during the interview, and the GPT or Claude many not work as expected.
Want to practice in real environment? try adeptai.dev
Critically, here what they’re grading is problem solving, code quality, and verification, also how well you prompt the AI. BUT, the AI is there so you don’t get bottlenecked on syntax, the bar is whether you can navigate a codebase, spot when the AI’s output is wrong, and defend your decisions.
One candidate also stresses spending the first few minutes decomposing the problem before touching the AI at all! Vague prompts read as junior thinking, specific ones read as senior judgment.
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Four week plan
Week 1
Vocabulary, not mastery. You don’t need to derive algorithms cold. You need enough pattern recognition to read AI-generated code and know if it’s doing something dumb (O(n²) when O(n) was obvious, off-by-one on boundaries, wrong data structure for the lookup). Go through NeetCode’s roadmap or Blind75, but don’t grind to memorize for each pattern (two pointers, sliding window, hashmap lookups, BFS/DFS, basic DP), just get to “I recognize this shape and know its complexity” not “I can write it blind.”
Week 2
Pair with AI on mediums, badly on purpose. Pick 8–10 medium leetcode problems. Ask Claude or ChatGPT to solve them, but your job isn’t to accept the output, it’s to read every line, trace through 2–3 test cases by hand before running anything, and find the bug before the test suite does. Some of these will pass first try, some won’t. That gap is the actual skill being tested.
Week 3
Join The Writer's Circle event Simulate the real format. Find a small unfamiliar open source repo or ask an AI to generate a small multi-file mock project. Practice: read code you didn’t write, ask the AI to explain a function, find and fix a planted bug, then extend it with a new feature. This is much closer to the real round than solving problems from scratch.
Week 4
Timed mocks, narrate everything. Full 60-minute runs. Force yourself to talk through: what the problem is asking, what you’re about to prompt for and why, what you’re checking for in the output, and what you’d test next. Recording yourself and listening back is more useful than another practice problem at this stage.
How do I use adeptAI to practice for the AI-assisted coding interview?
adeptAI.dev reproduces the actual interview conditions rather than a generic coding challenge: a multi-file project, a timed session, and a separate AI Assist panel you have to direct rather than a plain autocomplete box. To get the most out of a practice run:
Read the full problem brief before opening AI Assist. Each problem includes an interface contract (method signatures, expected return types, edge-case definitions) work out your approach from that alone first.
Write your plan down, even just a comment or scratch note, before touching the AI panel. This mirrors the “decompose first” phase Meta itself evaluates.
Prompt in scoped pieces. Ask AI Assist to implement one method or one piece of logic at a time rather than the whole problem, the same discipline expected in the real round.
Review every AI suggestion like a pull request — trace through a few cases by hand before running the test harness, since spotting a subtly wrong AI-generated implementation is a core part of what’s graded.
Use the timer as a real constraint. Treat each “Test Part” as a checkpoint; if you’re still iterating deep into the time budget, that’s a signal to simplify your approach rather than keep patching.
Debug from failing tests deliberately. When a test fails, narrate out loud or in writing what broke and why before asking the AI for a fix that verification step is exactly what interviewers are watching for.
Problems are rated by difficulty including Hard-tier, multi concept systems problems so you can ramp from foundational patterns up to interview realistic complexity as your prep progresses.
Does this round matter more or less for senior and management-track roles?
For more senior and engineering manager track interviews, the weighting tends to shift further toward judgment, verification, and communication, and further away from raw algorithmic speed. That plays to the strengths of candidates with strong systems and product thinking, even if their day-to-day coding has grown rusty as long as they can still read code critically and explain their reasoning clearly under time pressure.