RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances the accuracy and relevance of language model responses by incorporating information retrieved from external knowledge sources. Rather than relying solely on the knowledge embedded in a model's training data, RAG systems first search for relevant information from databases, documents, or knowledge bases, then use this retrieved information to inform and ground the model's response. This approach significantly reduces hallucinations and enables AI systems to access up-to-date information that wasn't available during training. RAG typically involves converting documents into vector embeddings stored in specialized databases, then using semantic search to find relevant content based on user queries. The retrieved information is then provided as context to the language model, which generates responses that are both factually grounded and contextually appropriate. RAG has become essential for enterprise AI applications where accuracy and current information are critical, enabling organizations to leverage their proprietary data while maintaining the conversational capabilities of large language models.