Large Language Models (LLMs) are remarkable at reasoning over the text you give them, but they have two hard limitations. One, their knowledge is frozen at training time, and second their context window can only hold so much. Vector stores are the piece of infrastructure that works around both problems, and they sit at the heart of retrieval-augmented generation (RAG), semantic search, and long-term memory for AI agents. This post covers what vector stores are, why they exist, and how they fit into an LLM application.
