Retrieval-Augmented Generation (RAG) is an AI technique that combines language generation with real-time information retrieval. It enhances the accuracy and relevance of AI responses by pulling contextually rich data from external sources. RAG is widely used to improve decision-making, reduce hallucinations in generative models, and adapt AI systems across various industries.