I agree with you that simple vector search + context stuffing is dead as a method, but I think it's ridiculous to reserve the term "RAG" for just the earliest most basic implementation. The definition of Retrieval Augmented Generation is any method that tries to give the LLM relevant data dynamically as opposed to relying purely on it memorising training data, or giving it everything it could possibly need and relying on long context windows.

The RAG system you mentioned is just RAG done badly, but doing it properly doesn't require a fundamentally different technique.

I don't think we should undersell that transformers and semantic search are really powerful information retrieval tools, and they are extremely potent for solving search problems. That being said, I think I agree with you that RAG is fundamentally just search, and the hype (like any hype) elides the fact that you still have to solve all of the normal, difficult search problems.