What you describe sounds like Agetic RAG https://zzbbyy.substack.com/p/agentic-rag

> The traditional way to do RAG is to find information relevant to a query - and then incorporate it into the LLM prompt together with the question we want it to answer.

Technically this is incorrect. The original RAG paper used a seq2seq generator (BART) and involved two methods: RAG sequence and RAG token.

RAG sequence used the same fixed documents and appended them to the input query (note, this is different from a decoder-only model). RAG token generates each token based on a different document.

I only nitpick this because if someone is going to invent new fancy-sounding variants of RAG they should at least get the basics right.

Traditional does not equate original. The original technique was never used widely and cannot be called the traditional way.

You’re right. Many people ppl e take a mental shortcut and assume that RAG is a vector DB search. Any kind of retrieval is retrieval. You can do keyword search. You can do a PageRank like query. You can sort content by date and send the most recent items to the LLM. It’s all retrieval. That is the R on Retrieval Augmented Generation.