Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique that combines information retrieval with language model generation to produce more accurate, grounded, and up-to-date responses. Instead of relying solely on a model's training data, RAG systems retrieve relevant information from external sources before generating responses. The RAG process typically works in three steps: First, a user query is converted to an embedding and used to search a vector database for relevant documents or passages. Second, the most relevant retrieved documents are included as context in the prompt to the language model. Third, the language model generates a response based on both the user query and the retrieved context. RAG addresses several key limitations of standalone language models. It reduces hallucinations by grounding responses in verified sources, enables access to current information beyond the model's training data, allows models to cite sources for their claims, and can improve accuracy on domain-specific questions by retrieving specialized knowledge. RAG is particularly valuable for applications requiring factual accuracy, such as customer support, medical information, legal guidance, and research assistance. Implementing RAG requires several components: a collection of documents or knowledge base, an embedding model to convert text to vectors, a vector database to store and search embeddings, and a language model for generation. The quality of RAG systems depends on the quality of retrieved documents, the relevance of the retrieval mechanism, and how well the language model integrates retrieved information. RAG has become a standard approach for building reliable AI systems that need to provide accurate, current, and verifiable information.