Structure your answer around a clear workflow: first restate the problem and clarify requirements, then decompose it into subproblems, use the AI to accelerate specific parts (e.g., boilerplate, edge cases), critically review all suggestions, and finally integrate and test the solution. Emphasize that you remain the driver, using AI as a tool while maintaining ownership of correctness and performance.
Pro tip: Treat the AI as a junior engineer: give it precise, scoped prompts, ask it to explain its reasoning, and never accept code without understanding it. This shows you can leverage AI without sacrificing rigor—a key signal at Meta.
Restate the problem in your own words, ask clarifying questions, and break it into smaller subproblems (e.g., input parsing, core algorithm, edge cases). Identify which parts are well-suited for AI assistance.
Craft specific prompts that include constraints, expected input/output, and desired approach. Ask for explanations or multiple solutions to compare, and iterate on prompts if the first response is off-target.
Evaluate AI-generated code for correctness, efficiency, and edge cases. Look for logical errors, off-by-one mistakes, or suboptimal complexity, and verify against your own understanding.
Adapt the AI's suggestions into your solution, ensuring consistency with your overall design. Write tests (including edge cases) and run them to validate correctness and performance.
Explain your decisions, trade-offs, and how you used AI to the interviewer. Highlight what you learned and how you ensured the final solution met requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.