Retrieval-Augmented Generation (RAG) is an AI technique that enhances large language models (LLMs) by combining them with external information retrieval systems. Instead of relying solely on the model’s pre-trained knowledge, RAG retrieves relevant documents or data from sources like databases, APIs, or vector stores, and feeds this context into the model to improve accuracy and reliability. This approach is widely used in applications such as chatbots, enterprise search, and knowledge assistants, where up-to-date or domain-specific information is required. RAG reduces hallucinations, enables better grounding in facts, and allows models to scale with evolving data without retraining. Its flexibility and effectiveness make it a popular architecture for organizations seeking trustworthy, context-aware AI solutions.