RAG for a Codebase with 10k Repos

Recent advancements in generative AI coding, particularly the implementation of Retrieval Augmented Generation (RAG), show promise for enterprise-level applications, though they face unique challenges such as scalability and contextual awareness. The company qodo, previously known as Codium, has been exploring RAG to enhance AI coding platforms by ensuring code quality and integrity across large, complex codebases. This involves strategies like intelligent chunking, where code is divided using static analysis to maintain contextual integrity, and the generation of natural language descriptions to improve code retrieval. qodo addresses issues such as incomplete or irrelevant code segments by refining chunking methods and embedding enhancements, which are crucial for maintaining context and relevance in large code repositories. Additionally, they employ advanced retrieval techniques that leverage language models for filtering and ranking code snippets, improving the relevance of query results. The system is designed to scale across thousands of repositories, implementing repo-level filtering to manage noise and inefficiency. The company also focuses on developing evaluation benchmarks in collaboration with enterprise clients to assess the performance of RAG systems, aiming to boost developer productivity and code quality in large organizations.