AI what is RAG

AI what is RAG refers to Retrieval-Augmented Generation, a fundamental artificial intelligence methodology that enhances language model capabilities by combining external knowledge retrieval with generative AI technologies. RAG in AI represents systems that can dynamically access and incorporate information from external databases, documents, and knowledge repositories during response generation, rather than relying solely on pre-trained model parameters. This approach addresses critical AI limitations including knowledge cutoffs, hallucinations, and outdated information by grounding outputs in current, verifiable sources. AI RAG operates through technical components including embedding models for semantic understanding, vector databases for efficient retrieval, and integration mechanisms that combine external knowledge with generative processes. 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 accurate, up-to-date information for autonomous decision-making and complex workflow execution.