LLMs are trained on static data. Ask one about your internal docs, last week's meeting notes, or a product spec — it has no idea. RAG solves this by fetching relevant content at query time and injecting it into the prompt. The model still does the reasoning; you just give it …

A vector store is a database that finds things by meaning, not exact match. You convert text into a numerical vector (an embedding), store it, and later search for semantically similar vectors. That's the retrieval half of RAG. Which store you pick depends on scale, whether you want persistence, and …