RAG AI definition

RAG AI definition describes Retrieval-Augmented Generation, a sophisticated artificial intelligence architecture that combines information retrieval capabilities with generative language models to produce accurate, contextually grounded responses. RAG AI systems operate by first retrieving relevant information from external knowledge sources using semantic search, then incorporating this retrieved context into the generation process to create informed outputs. The methodology addresses fundamental limitations of standalone large language models, including knowledge cutoffs, factual inaccuracies, and hallucinations by grounding responses in verified external data. Key technical components include embedding models for query and document vectorization, vector databases for efficient similarity search, retrieval algorithms for context selection, and integration mechanisms that combine retrieved information with generative capabilities. RAG AI enables access to real-time information, proprietary datasets, and domain-specific knowledge while maintaining natural language generation quality. This approach has become crucial for enterprise AI applications, particularly in agentic AI systems where autonomous agents require accurate, current information for decision-making and task execution across complex business workflows.