RAG definition AI

RAG definition AI refers to Retrieval-Augmented Generation, a fundamental artificial intelligence methodology that enhances language model performance by integrating external knowledge retrieval with generative capabilities. This approach defines a new paradigm in AI where systems first search through indexed knowledge bases to retrieve contextually relevant information, then use this retrieved content to augment the generation process, producing more accurate and factually grounded responses. RAG definition encompasses the technical architecture combining embedding models for semantic understanding, vector databases for efficient information storage and retrieval, and sophisticated ranking mechanisms for relevance optimization. The methodology addresses core AI challenges including knowledge limitations, temporal constraints, and hallucination tendencies by anchoring responses in verifiable external sources. RAG systems enable AI applications to access current information, proprietary databases, and specialized domain knowledge while preserving natural language generation quality. This architecture has become essential for enterprise AI implementations, particularly in agentic AI systems requiring reliable, up-to-date information for autonomous decision-making and complex workflow management.