RAG

RAG (Retrieval-Augmented Generation) is a technique that enhances LLM responses by first retrieving relevant information from a knowledge base before generating an answer. This combines the strengths of search systems with generative AI, enabling accurate and up-to-date responses grounded in specific data rather than relying solely on the model's training data.