My First-Hand Experience with Meta’s New AI-Enabled Interview

2025-11-18

Note: Information below was shared by a candidate who went through Meta's AI-Enabled coding interview recently.

In 2025, Meta started rolling out a brand-new interview format: the AI-Enabled coding round. I recently had the chance to experience it first-hand.

What Is Meta’s AI-Enabled Coding Round?

Meta’s new AI coding round is designed to simulate real-world debugging and problem-solving with the help of an integrated AI assistant. You get access to a small codebase, a few failing test cases, and a built-in AI helper that can explain, summarize, or even suggest code, but only if you use it wisely.

Not all programming languages are supported (I learned this the hard way). I originally wanted to code in a different language, but had to fall back to Python, which was one of the supported options.

The difficulty level of my task felt similar to the official practice questions Meta provides, so don’t expect a crazy leap in difficulty: the challenge lies more in how you collaborate with the AI.

How the Interview Worked

Here’s a play-by-play of what actually happened:

  • The interviewer started with a quick tour of the environment: how to run code, view outputs, and check test results.

  • As soon as I hit “Run,” I saw several test failures.

  • My first instinct? Ask the AI to explain the codebase architecture.

    • The interviewer stopped me and said, “Try to figure it out yourself first. Follow the test failures.”
    • So I did. After digging into the source files, I pinpointed the bug and confirmed my understanding by asking the AI about a specific code section. It gave a decent explanation, and I fixed the bug.

Then I asked a bold question:

“Can I just ask the AI to solve the whole problem?”

The interviewer said, “You can, but it might be wrong.” Fair enough. I told them I’d review everything the AI suggested.

Working Through the Problem

Once the first bug was gone, my next goal was to implement a solver.

I asked if I could use the AI to help me understand the remaining code structure. The interviewer encouraged me to continue exploring manually, so I did. There were about five source files total, not too bad.

To double-check, I still asked the AI for a code summary, just to make sure my mental model matched. It did.

When it came to writing the solver:

  • I first paraphrased the long problem description into a shorter prompt for the AI.
  • The interviewer told me to explain my own approach first, so I outlined a brute-force method.
  • We both agreed it would likely be too slow for larger datasets.
  • Then I brainstormed with the AI, combining my idea with its suggestion of a backtracking with branch-cutting approach.

I let the AI write the initial code, reviewed it carefully, and it actually passed the basic test cases!

The Real Challenge: Scaling Up

Next, we tested it against larger datasets from different files, and that’s when the AI-generated solution started timing out.

I asked the AI to help optimize it using memoization or dynamic programming, but honestly, the suggestions were underwhelming. It clearly struggled with advanced optimization logic.

So I went back to my own reasoning and added more refined branch-cutting logic, which helped pass another big test case.

Unfortunately, the largest test case was still running when time ran out. I discussed my new optimization plan with the interviewer, who thought it was promising, but I didn’t get to implement it before the session ended.

My Takeaways

Honestly, the built-in AI assistant felt weaker than ChatGPT-3, but it wasn’t useless. With precise prompt, it could:

  • Summarize unfamiliar code,
  • Explain tricky logic sections,
  • And provide a starting point for brainstorming solutions.

That said, you still need strong fundamentals. The AI was not smart enough to solve the problem on its own. If you rely on it blindly, you’ll hit dead ends fast.

Final Thoughts

Meta’s AI-assisted coding round feels like a glimpse into the future of technical interviews. It doesn’t replace traditional coding rounds. It tests how you think, debug, and collaborate with AI.

If you’re preparing for it, practice:

  • Reading and understanding medium-sized codebases,
  • Writing concise prompts that extract the most from AI,
  • And knowing when to ignore or refine the AI’s answers.

For more in-depth detailed insights, you can find experiences shared by other candidates who have done the interviews recently here.