RAG meaning in AI

RAG meaning in AI refers to Retrieval-Augmented Generation, defining a fundamental methodology within artificial intelligence that enhances system capabilities by integrating external knowledge retrieval with generative language models. This meaning encompasses how AI systems transcend traditional limitations by dynamically accessing and incorporating information from external databases, documents, and knowledge repositories during response generation.

RAG meaning in AI represents the shift from static, pre-trained models to dynamic, knowledge-connected systems that can reference current information, proprietary data, and domain-specific content to produce accurate, contextually relevant outputs. The meaning includes technical architecture involving vector embeddings for semantic understanding, retrieval mechanisms for information access, and integration frameworks that combine external knowledge with AI generation processes. RAG meaning in AI signifies enhanced accuracy, reduced hallucinations, and improved reliability essential for enterprise applications requiring factual precision, up-to-date information access, and autonomous decision-making capabilities.