Retrieval-Augmented Generation (RAG) system

Retrieval-Augmented Generation (RAG) system is an AI architecture that enhances large language model responses by dynamically retrieving relevant information from external knowledge bases before generating output. The system operates through two core components: a retrieval mechanism that searches vector databases or document repositories for contextually relevant data, and a generation component that synthesizes this retrieved information with the model’s pre-trained knowledge to produce accurate, up-to-date responses. RAG systems address knowledge limitations in traditional LLMs by providing access to current information, domain-specific data, and proprietary content that wasn’t included in the model’s original training dataset. This hybrid approach significantly reduces hallucinations, improves factual accuracy, and enables AI applications to maintain knowledge currency without requiring costly model retraining. RAG architectures are particularly valuable for enterprise applications requiring access to internal documentation, real-time data integration, and specialized domain knowledge where static training data proves insufficient.