> But here is the thing, if you rely on RAG or any other knowledge injection for recommendation, you essentially make your LLM parrot machine, no better than a search engine, just much slower.

this is a really good point. I was working on some careful q/a data curation today that is then fed to a vector store where embeddings are calculated and served. I realized that my carefully curated q/a data in combination with the vector database works just fine for what i want to do all by itself. A really good semantic search of my q/a database turns up answers to my questions with no rag llm prompting required. When I added the llm it just put the same information in different words, not super useful when i could have just looked at the returned embeddings and gotten the same information.