Meta·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Meta SWE interview where the format was a bit unusual. You get a code snippet, run it through an AI prompt to get a review, and then your job is to critically evaluate what the AI actually said. Accepting the valid stuff, pushing back on hallucinated bugs, and catching whatever the AI missed.

Questions Asked (1)

You are given a real code snippet. Use an AI assistant as a senior engineer reviewer with a specific prompt, then critically evaluate the AI's output: accept valid fixes, reject hallucinated bugs, and identify edge cases the AI missed.

Algorithms & Data StructuresTechnical Trade-offsRoot Cause Analysis

Author's notes

The weird part isn't writing the prompt, it's that you have to actually disagree with the AI on the spot.

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AI HintsAI Generated

Suggested Approach

Demonstrate a structured review process: first craft a precise prompt to the AI that includes the code, context, and specific review goals, then critically evaluate the AI's response by verifying each claim against the code and your own knowledge. Highlight where the AI is correct, where it hallucinates, and what edge cases it missed, showing you can leverage AI while maintaining engineering rigor.

Pro tip: Treat the AI as a junior engineer: its suggestions are starting points, not gospel. Always verify with tests or manual reasoning, and explicitly call out when the AI's confidence is misplaced.

1. Craft a precise prompt

Write a prompt that gives the AI the code, the context (e.g., language, purpose), and asks for specific types of issues (bugs, performance, edge cases). This sets the stage for a focused review.

2. Analyze AI output for validity

Go through each point the AI raises. For each, determine if it's a real issue by tracing the code or reasoning about inputs. Accept valid fixes and reject hallucinations with justification.

3. Identify missed edge cases

Think about inputs or scenarios the AI didn't consider, such as empty inputs, boundary values, concurrency, or error conditions. Explain why they matter.

4. Propose a corrected solution

Synthesize the valid AI suggestions with your own fixes for missed edge cases into an improved version of the code, explaining the changes.

5. Reflect on the process

Summarize lessons learned about using AI as a reviewer, such as the importance of verification and the types of errors AI tends to make.

Key Points to Mention

Prompt engineering: being specific about the review goals and providing sufficient context to get useful output.
Critical evaluation: distinguishing between valid concerns, false positives, and stylistic preferences.
Edge case analysis: considering inputs like null, empty, large values, and concurrent access.
Root cause analysis: going beyond symptoms to understand why a bug occurs and how to prevent it.
Trade-offs: balancing correctness, performance, and readability when applying fixes.
Testing: how you would verify the fixes, such as writing unit tests for the edge cases.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.