I have proclaimed RAG is dead many times, and I stand by it.
RAG is Dead! Long Live Agentic RAG! || Long Live putting stuff in databases where it damn well belongs!
I think you agree with the people saying RAG is Dead, or at least you agree with me and I say RAG is Dead, when you say "Simply using docling and transforming PDFs to markdown and have a vector database doing the rest is ridiculous."
I fully agree, but that was the promise of RAG, chunk your documents into little bits and find the bit that is closet to the users query and add it to the context, maybe leave a little overlap on the chunks, is how RAG was initially presented, and how many vendors implement RAG, looking at tools like Amazon Bedrock Knowledge Bases here.
When I want to know the latest , I want that pulled that from the source of truth for that data, not hopefully get the latest and not last years number from some document chunk.
So, when people, or at least when I say RAG is Dead, it's short hand for: this is really damn complex, and vector search doesn't replace decades of information theory, storage and retrieval patterns.
Hell, I've worked with teams trying to extract everything from databases to push it into vector stores so the LLM can use the data.
First, it often failed as they had chunks with multiple rows of data, and the LLM got confused as to which row actually mattered, they hadn't realized that the full chunk would be returned and not just the row they were interested in.
Second, the use cases being worked on by these teams were usually well defined, that is, the required data could be deterministically defined before going to the LLM and pulled from a database using a simple script, no similarity required, but that's not the cool way to do it.