[ankit219]

I have a different pov on retrieval. It's a hard problem to solve in a generalizable format with embeddings. I believe this can be solved at a model level where its used to fix an issue. With the model providers (oai, anthropic) going full stack, there is a possibility they solve it at reinforcement learning level. Eg: when you teach a model to solve issues in a codebase, the first step is literally getting the right files. Here basic search (with grep) would work very well as with enough training, you want the model to have an instinct about what to search given a problem. similar to how an experienced dev has that instinct about a given issue. (This might be what the tools like cursor are also looking at). (nothing against anyone, just sharing a pov, i might be wrong)

However, the fast apply model is a thing of beauty. Aider uses it and it's just super accurate and very fast.

[bcyn]

Definitely agree with you that it's a problem that will be hard to generalize a solution for, and that the eventual solution is likely not embeddings (at least not alone).

[cocoflunchy]

Relevant interview extract from the Claude Code team: https://x.com/pashmerepat/status/1926717705660375463

> Boris from the Claude Code team explains why they ditched RAG for agentic discovery. > "It outperformed everything. By a lot"

[ankit219]

This is very cool. They explained the solution better than I did. If I knew, I would have just linked this :)