Retrieval-Augmented Generation(RAG)

In detail

Retrieval-Augmented Generation (RAG) combines a search step (retrieval) with a generation step. The system converts your documents into vector embeddings and stores them in a vector database. When a user asks a question, the system retrieves the most relevant chunks and feeds them to a language model along with the question, so the model answers using your content rather than its general training data. RAG is the dominant pattern for AI assistants that need to know about a specific organisation's policies, products or knowledge base.