RAG acronym AI

RAG acronym AI refers to Retrieval-Augmented Generation, a critical artificial intelligence architecture that addresses limitations of standalone large language models by integrating external knowledge retrieval with generative capabilities. The RAG framework operates through a two-stage process: first retrieving contextually relevant information from external data sources using vector similarity search, then augmenting the language model’s prompt with this retrieved context to generate informed, factually grounded responses. This approach enables AI systems to access current information, domain-specific knowledge, and proprietary datasets that weren’t included in the model’s original training data. RAG implementations leverage embedding models to convert queries and documents into vector representations, vector databases for efficient similarity matching, and sophisticated chunking strategies to optimize information retrieval. The technique has become fundamental in enterprise AI applications, particularly for agentic AI systems requiring accurate, up-to-date information for autonomous decision-making. Modern RAG variants include advanced techniques like hierarchical retrieval, query rewriting, and multi-modal retrieval for enhanced performance and accuracy.