What is AI RAG

What is AI RAG refers to Retrieval-Augmented Generation in artificial intelligence, a methodology that enhances language model capabilities by combining external knowledge retrieval with generative AI technologies. AI RAG systems operate by first retrieving relevant information from external databases, documents, and knowledge repositories using semantic search, then incorporating this retrieved context into the generation process to create informed, accurate outputs. This approach addresses fundamental AI limitations including hallucinations, knowledge gaps, and outdated information by grounding responses in current, verifiable sources. AI RAG consists of technical components including embedding models for semantic understanding, vector databases for efficient retrieval, and integration mechanisms that combine external knowledge with generative capabilities. The methodology enables AI systems to access real-time information, proprietary datasets, and domain-specific knowledge while maintaining natural language generation quality, making it essential for enterprise applications requiring reliable, up-to-date information for autonomous decision-making.