RAG Retrieval Augmented Generation

RAG Retrieval Augmented Generation is a hybrid artificial intelligence architecture that combines the generative capabilities of Large Language Models with external knowledge retrieval systems to produce more accurate, contextually relevant, and factually grounded responses. This framework addresses the inherent limitations of standalone LLMs, including knowledge cutoffs, hallucinations, and inability to access real-time information, by implementing a two-stage process that first retrieves relevant information from external databases and then augments the generation process with this retrieved context. The architecture operates through semantic search mechanisms that identify and extract pertinent passages from vector databases, knowledge graphs, or document repositories based on query similarity matching. These retrieved passages are then incorporated into the prompt context before being processed by the generative model, enabling the system to produce responses that are both linguistically coherent and factually accurate. Core components include embedding models for vectorizing queries and documents, vector databases for efficient similarity search, retrieval algorithms for identifying relevant context, and prompt engineering techniques that effectively combine retrieved information with user queries. This approach enables organizations to build AI systems that can access proprietary knowledge bases, provide up-to-date information, and maintain transparency through source attribution while leveraging the natural language generation capabilities of modern LLMs.