A checklist for reviewing AI-generated code

Introduction

AI output tends to look confident and be subtly wrong, so this checklist focuses on where it usually breaks: error handling, boundary conditions, and invented APIs.

Field notes

A few patterns we kept seeing while puttering around AI problems:

• The simple version survived every refactor; the clever version didn’t.
• Documentation written during the work aged far better than documentation written after.
• The second time a problem shows up, it is already a pattern — name it.

A checklist you can steal

• The failure is reproducible with one command.
• The fix has a test that fails without it.
• Someone other than the author has run it on their machine.

Closing

Write the short version future-you will actually read. That is the whole trick.