Developer explores using RAG systems to retain and enforce coding standards in AI agents across large codebases

A developer questions whether piping LLM outputs into a RAG (Retrieval-Augmented Generation) stack could improve agent memory retention

The proposal aims to help AI agents better adhere to coding guidelines and policies within large codebases over extended periods

The inquiry seeks feedback on this general concept beyond the specific use case of code compliance