What is RAG in AI

What is RAG in AI refers to Retrieval-Augmented Generation, a foundational methodology within artificial intelligence that enhances language model capabilities by integrating 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, transcending traditional model limitations. This approach addresses critical AI challenges including knowledge cutoffs, hallucinations, and outdated information by grounding outputs in current, verifiable sources. RAG in AI 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.