One thing I'd add is that RAG quality depends just as much on the retrieval pipeline as the vector database itself. Chunking strategy, metadata filtering, reranking, and context assembly often have a bigger impact on answer quality than swapping one vector DB for another. I've seen teams blame the LLM for hallucinations when the real issue was that the wrong chunks were being retrieved. "Good notes" only help if you're handing the model the right notes. Nice explanation overall it makes a concept that's often overcomplicated much easier to grasp.