RAG stands for Retrieval Augmented Generation. It’s a technique that gives AI models access to external data — your documents, databases, or knowledge bases — so they can answer questions about information they weren’t trained on.
Instead of relying only on what the model learned during training, RAG retrieves relevant information from your data at query time and feeds it to the model as context. This reduces hallucinations and keeps answers grounded in facts.
RAG is how modern AI products like chatbots, search engines, and enterprise AI tools work under the hood.