Retrieval-augmented generation (RAG) is a technique that enables LLMs to retrieve and incorporate new information from external data sources. Subsequently, they respond to a user query.

Vector databases are a key element of RAG. They store document chunks, articles, and knowledge bases as embeddings. When a user asks a question, the system finds relevant text chunks by comparing vector similarity. It feeds them to a LLM to generate responses using the retrieved information.