My codebase is relatively greenfield (started working on it early last year) but it’s up to ~50k lines in a mixed C++/Rust codebase with a binding layer whose A

My codebase is relatively greenfield (started working on it early last year) but it’s up to ~50k lines in a mixed C++/Rust codebase with a binding layer whose API predates every LLM’s training sets. Even when I started ChatGPT/Claude weren’t very useful but now the project requires a completely different strategy when working with LLMs (it’s a QT AI desktop app so I’m dogfooding a lot). I’ve also used them in a larger codebase (~500k lines) and that also requires a different approach from the former. It feels a lot like the transition from managing 2 to 20 to 200 to 2000 people. It’s a different ballgame with each step change. A very well encapsulated code base of ~500k lines is manageable for small changes but not for refactoring, exploration, etc, at least until useful context sizes increase another order of magnitude (I keep trying Gemini’s 2M but it’s been a disappointment). I have a lot of documentation aimed at the AI in `docs/notes/` (some of it written by an LLM but proofread before committing) and I instruct Cursor/Windsurf/Aider via their respective rules/config files to look at the documentation before doing anything. At some scale that initial context becomes just a directory listing & short description of everything in the notes folder, which eventually breaks down due to context size limits, either because I exceed the maximum length of the rules or the agent requires pulling in too much context for the change. I’ve found that there’s actually an uncanny valley between greenfield projects where the model is free to make whatever assumptions it wants and brownfield projects where it’s possible to provide enough context from the existing codebase to get both API accuracy (hallucinations) and general patterns through few-shot examples. This became very obvious once I had enough examples of that binding layer. Even though I could include all of the documentation for the library, it didn’t work consistently until I had a variety of production examples to point it to. Right now, I probably spend as much time writing each prompt as I do massaging the notes folder and rules every time I notice the model doing something wrong.