RAG stands for Retrieval-Augmented Generation. It sounds technical, but the idea is simple: when you ask an AI agent a question, the system first searches for relevant sections from your documents (contracts, internal manuals, price lists, conversation history) and then formulates its answer based on that.
Without RAG, AI can only answer based on what it learned during training – which may be outdated or too general. With RAG, the agent works with your current data.
Example: a customer support agent with RAG can answer "What's the price of product X for customers with 30-day payment terms?" – because it has access to your price list. Without RAG, it would answer generically or simply make up the information.
RAG is now the standard component of most enterprise AI solutions. Because businesses don't need general knowledge – they need AI that knows their processes, products, and customers.