RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines information retrieval with text generation. Instead of relying solely on an AI model's training data, RAG systems first search a knowledge base for relevant documents, then provide those documents as context when generating responses. This grounds the AI's answers in specific, verifiable information. For non-technical readers, imagine asking an expert a question. A traditional AI is like an expert who answers from memory alone, which might be outdated or incomplete. RAG is like an expert who first looks up the relevant documents, reads them, and then answers based on that specific information. The result is more accurate, current, and verifiable answers. RAG addresses a fundamental limitation of AI models: they can only know what they were trained on, and training is expensive and time-consuming. With RAG, you can give an AI access to your latest documentation, policies, or data without retraining the model. This makes AI assistants practical for business use, where information changes frequently.