LLM Infrastructure
RAG Isn't a Vector Database. It's a Retrieval Problem.
Every team building with RAG right now has roughly the same setup. Documents get chunked, chunks get embedded, embeddings go into a vector database, and at query time you run a similarity search, grab the top-k chunks, and stuff them into the prompt. It works well enough to demo.