RAG means in AI

RAG means in AI refers to Retrieval-Augmented Generation, a methodology that signifies enhanced artificial intelligence capabilities through the integration of external knowledge retrieval with generative language models. This meaning represents AI systems that can dynamically access and incorporate information from external databases, documents, and proprietary knowledge repositories during response generation, transcending traditional model limitations.

RAG means in AI encompasses technical architecture involving vector embeddings for semantic search, retrieval mechanisms for information access, and context augmentation that combines external knowledge with generative processes. The significance means improved accuracy, reduced hallucinations, and access to current information beyond training data cutoffs. RAG means in AI represents the evolution from static, knowledge-limited systems to dynamic, information-connected applications that can reference real-time data, domain-specific content, and proprietary information essential for enterprise applications requiring factual precision and autonomous decision-making capabilities.