alt Hacker NewsRAG is a well-known technique now, and to paraphrase Emily Bender[1], here are some reasons why it's not a solution.
The code extruded from the LLM is still synthetic code, and likely to contain errors both in the form of extra tokens motivated by the pre-training data for the LLM rather than the input texts AND in the form of omission. It's difficult to detect when the summary you are relying on is actually missing critical information.
Even if the set up includes the links to the retrieved documents, the presence of the generated code discourages users from actually drilling down and reading them.
This is still a framing that says: Your question has an answer, and the computer can give it to you.
1 https://buttondown.com/maiht3k/archive/information-literacy-...
We actually don't use RAG! It's not that good as you say.
We build a description of the codebase including the file tree and parsed function names and class names, and then just ask Haiku which files are relevant!
This works much better and doesn't require slowly creating an index. You can just run Codebuff in any directory and it works.