RAG (Retrieval-Augmented Generation) is an advanced technique used in generative AI services that combines information retrieval with content generation. It connects AI models to external or real-time data sources, allowing them to fetch relevant information before generating responses. This ensures outputs are more accurate, up-to-date, and context-aware. By merging retrieval and generation, RAG enhances response quality, reduces hallucinations, and is especially useful for knowledge bases, chatbots, and enterprise applications.