RAG (Retrieval-Augmented Generation)

RAG enhances LLM outputs by first retrieving relevant documents from a knowledge base, then using them as context for generation. This reduces hallucinations and allows AI to access up-to-date information beyond its training data. RAG is widely used in enterprise AI assistants, customer support bots, and knowledge management systems.