Refactoring Sweep AI Code for Production

Sweep handles targeted refactoring issues well but may not complete large refactors that span many files consistently. It may update the definition but miss some usage sites, particularly in dynamic language code where static analysis can't find all references

Break large refactoring issues into smaller, more targeted issues for Sweep. Verify completion with a codebase search after each Sweep refactoring PR. Follow up with a cleanup pass if Sweep missed some usage sites

Not all Sweep AI code needs refactoring. If it works, is secure, and you don't need to modify it, leave it alone. Refactor when: you need to add features and the current structure makes it difficult, bugs keep appearing in the same area of code, or onboarding new developers takes too long because the code is hard to understand.

The golden rule of refactoring: behavior stays the same, structure changes. Before refactoring any code, make sure you have tests covering the current behavior (or add them first). Refactor in small steps — change one thing at a time and verify the app still works. Use version control (git) to commit after each successful change so you can revert if something breaks.

A well-organized project has a predictable structure where anyone can find what they're looking for. Group files by feature (not by type), keep related code together, and use clear naming. Every file should have a single clear purpose. If you can't describe what a file does in one sentence, it's probably doing too much and should be split.