A disciplined workflow for testing, understanding, and repairing AI-suggested code.
Reproduce the problem
Capture the exact error, steps to reproduce it, and package or environment versions.
Inspect surrounding code
Check assumptions about data, permissions, paths, and network responses.
Ask for a minimal fix
Share relevant code and the error, and ask for a small change with an explanation. Do not paste secrets.
Run checks after each change
Use tests, linting, type checks, and a build where available.
Review security implications
Check authorization, validation, dependencies, and error handling as well as whether the error disappeared.
Editorial note: This guide is for general educational purposes. Verify important claims with authoritative sources before publication or action.