To a certain extent you are probably still not using it optimally if you are still doing that much work to clean it up. We, for example, asked the LLM to analyze the codebase for the common patterns we use and to write a document for AI agents to do better work on the codebase. I edited it and had it take a couple of passes. We then provide that doc as part of the requirements we feed to it. That made a big difference. We wrote specific instructions on how to structure tests, where to find common utilities, etc. We wrote pre-commit hooks to help double check its work. Every time we see something it’s doing that it shouldn’t, it goes in the instructions. Now it mostly does 85-90% quality work. Yes it requires human review and some small changes. Not sure how the thing works that it built? Before reviewing the code, have it draw a Mermaid sequence diagram.

We found it mostly starts to abandon instructions when the context gets too polluted. Subagents really help address that by not loading the top context with the content of all your files.

Another tip: give it feedback as PR comments and have it read them with the gh CLI. This is faster than hand editing the code yourself a lot of times. While it cleans up its own work you can be doing something else.

Interesting, I actually do have a coding-guidelines.md file for that purpose, but I hadn't thought of having the LLM either generate it, or maintain it; good idea! :-)