The Developer’s Guide to Debugging AI-Generated Code

AI coding assistants can rapidly generate large amounts of software, but their output may contain hallucinated functions, deprecated APIs, flawed logic, inadequate edge-case handling, security vulnerabilities, and performance problems because models may lack current documentation and project-specific context. Debugging this code requires more than traditional line-by-line review, combining syntax and import checks, verification against official API documentation, logic and edge-case testing, static and dynamic security analysis, profiling, and manual review. The text recommends deciding between refactoring and rewriting based on whether the generated code’s core approach is sound or fundamentally misaligned with requirements or architecture. It also emphasizes validating AI-generated API code with captured production traffic and replay tools such as Proxymock, which can support deterministic testing, mock services, behavior comparison, and load testing. Better prompts that specify the technology stack, current versions, data structures, expected outputs, error handling, and testing requirements can reduce errors, but AI-generated code should still be treated as a useful starting point that requires layered validation before production deployment.

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