While LLMs make writing code easier, I have found that keeping it maintainable requires a disciplined approach. Here are some of the practices I follow to keep my AI-generated codebases clean and easy to manage:
- Versioned prompts: Save useful feature prompts in files. Version them.
- Automatic formatting: Use automated formatting tools such as Prettier, ESLint, or clang so the model focuses on logic, not layout.
- Linting and type checks: Run static analysis after each AI change. Catch unused variables, missing types, and unsafe imports early.
- Constant code review: Treat every AI change like a pull request from a junior developer. Read diffs and ask the model to explain decisions before merging.
- Git discipline: Commit and branch often. Small, isolated commits make it easy to revert or compare outputs.
- Unit tests: Generate them after the code works and is verified. Use tests to lock in correct behavior and prevent regressions.
- Context hygiene: Do not use AI for linting or formatting. Compact context often. Keep the context focused on reasoning and refactoring.
- Do not be afraid to throw away AI code: If it feels wrong, start over. Frequent commits make it safe to revert. Regenerating code costs seconds. Maintaining it costs hours.