A great idea, bypassing as much conversion as possible between vector space and natural language tokens. Reminds me of a discussion of having AI’s “talk” to each other using vector space.

There was an interesting quote “plain old BM25 from 1994 outperforms vector search on recall” and super relevant to what I did yesterday. I am trying to use small local models more often and yesterday I wrote Common Lisp code that uses a large corpus of text and a user query or prompt to construct a fairly concise one-shot prompt with select context from the text corpus. This is RAG, and I used both BM25 and vector embeddings matching. I added the code and an example as a new chapter in my CL book (link directly to new material: https://leanpub.com/lovinglisp/read#leanpub-auto-autocontext...) yesterday afternoon. BM25 is fast. This is new code, and I will certainly be experimenting more with it, but as-is it is useful when working with small local LLMs.