Retrieval-Augmented Generation (RAG) is an advanced AI architecture that combines the language fluency of LLMs with the precision of real-time data retrieval. Instead of relying on a model’s training data (which may be outdated or generic), RAG fetches relevant information from your own documents, databases, or knowledge base before generating a response — resulting in answers that are accurate, current, and domain-specific.