This article really resonates with me - I've heard people (and vector database companies) describe transformer embeddings + vector databases as primarily a solution for "memory/context for your chatbot, to mitigate hallucinations", which seems like a really specific (and kinda dubious, in my experience) use case for a really general tool.
I've found all of the RAG applications I've tried to be pretty underwhelming, but semantic search itself (especially combined with full-text search) is very cool.
I dare say RAG with vector DBs is underwhelming because embeddings are not underrated but appropriately rated, and will not give you relevant info in every case. In fact, the way LLMs retrieve info internally [0] already works along the same principle and is a large factor in their unreliability.
I've found all of the RAG applications I've tried to be pretty underwhelming, but semantic search itself (especially combined with full-text search) is very cool.