| Fair point Jeff -- you're right that we're still doing retrieval. The key distinction is how we retrieve. Traditional RAG for code uses vector embeddings and similarity search. We use filesystem traversal and AST parsing - following imports, tracing dependencies, reading files in logical order. It's retrieval guided by code structure rather than semantic similarity. I highly recommend checking out what the Claude Code team discovered (48:00 https://youtu.be/zDmW5hJPsvQ?si=wdGyiBGqmo4YHjrn&t=2880). They initially experimented with RAG using embeddings but found that giving the agent filesystem tools to explore code naturally delivered significantly better results. From our experience, vector similarity often retrieves fragments that mention the right keywords but miss the actual implementation logic. Following code structure retrieves the files a developer would actually need to understand the problem. So yes -- I should have been clearer about the terminology. It's not "no retrieval" -- it's structured retrieval vs similarity-based retrieval. And with today's frontier models having massive context windows and sophisticated reasoning capabilities, they're perfectly designed to build understanding by exploring code the way developers do, rather than needing pre-digested embeddings. |
Indeed, industry at large sees RAG as equivalent to "vector indexes and cosine similarity w.r.t. input query", and the rest of the article explains thoroughly why that's not the right approach.