← Series B+ Startup Interview Insights
Started writing things out by hand because I assumed using AI would look lazy or like I was cheating.
Emphasize that AI-generated code is a starting point, not a final solution. Describe a systematic validation process combining automated testing, manual review, and runtime verification, tailored to backend concerns like data integrity and scalability. Highlight the importance of understanding the code's intent and edge cases.
Pro tip: Mention that you treat AI-generated code like a junior engineer's pull request: you review it critically, test it thoroughly, and never merge without understanding every line. This shows maturity and a collaborative mindset.
Before running anything, read the generated code to grasp its purpose, assumptions, and how it fits into the existing system. Identify potential mismatches with requirements or architectural patterns.
Run linters, type checkers, and static analysis tools to catch syntax errors, type mismatches, and common anti-patterns. This quickly surfaces obvious issues without execution.
Write or adapt unit and integration tests to verify functionality, edge cases, and error handling. Use test coverage to ensure critical paths are exercised.
Step through the code with a debugger or add logging to trace execution, especially for complex logic. Compare behavior against expected outcomes and check for race conditions or resource leaks.
Deploy to a staging environment and monitor performance, memory usage, and error rates under load. Validate against backend-specific concerns like database queries, API contracts, and concurrency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.