RAG in AI meaning

RAG in AI meaning refers to Retrieval-Augmented Generation within artificial intelligence systems, defining a methodology that transforms how AI applications access and utilize knowledge by integrating external information retrieval with generative capabilities. This meaning encompasses AI architectures that dynamically retrieve relevant context from external databases, documents, and knowledge repositories before generating responses, rather than relying exclusively on pre-trained model parameters.

RAG in AI meaning represents enhanced accuracy, factual grounding, and reduced hallucinations through semantic search, vector embeddings, and context augmentation technologies. The meaning includes technical components such as embedding models for query processing, vector databases for efficient retrieval, and integration mechanisms that combine external knowledge with AI generation processes. RAG in AI meaning signifies the evolution from static, knowledge-limited AI systems to dynamic, information-connected applications that can access current data, proprietary content, and domain-specific knowledge essential for enterprise applications requiring reliable, up-to-date information for autonomous operations.