With RAG, the user's question is first turned into a search that finds matching documents from a database or vector database. These documents are then inserted into the prompt so the model answers based on current, verifiable facts. RAG reduces hallucinations and makes it possible to use company-internal or up-to-date knowledge. The technique combines embeddings, semantic search, and text generation.
RAG (Retrieval-Augmented Generation)
RAG (retrieval-augmented generation) is a technique where an AI model retrieves relevant information from an external knowledge source before answering. This delivers current, fact-based answers without retraining.