The biggest risk with AI-generated code isn’t that it’s wrong — it’s that it’s almost right. A human reviewing it sees something that looks correct, skims it, and merges. The subtle bug lives on.
This is why I run a structured technical audit on every piece of AI-generated code before it enters production.
The Audit Checklist
1. Correctness vs. Plausibility
AI models are optimized to produce plausible text. For code, this means they generate something that looks like it should work, but often doesn’t handle edge cases.
I check: null inputs, empty arrays, concurrent access, error paths.
2. Hidden Dependencies
AI models sometimes invent APIs that don’t exist or import modules that aren’t in the project. I run the generated code through a type checker and a build before any logic review.
3. Security Surface
AI models trained on public code have seen plenty of insecure patterns. SQL injection, XSS, mass assignment — these show up regularly in generated code.
4. Maintainability
Generated code tends to be flat — long functions, repetitive logic, no abstraction. I flag functions over 50 lines and duplicated logic blocks.
Recovery Sprints
When an audit finds systemic issues, I run a “recovery sprint”: a focused session where I fix all instances across the generated codebase, then update the prompt template to prevent recurrence.
The Bottom Line
AI-generated code can be production-ready, but only with the same rigor you’d apply to any code contribution.
