Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is a framework that combines the strengths of retrieval-based and generative models to enhance text generation tasks. It utilizes an external knowledge base to retrieve relevant documents or passages, which are then used to inform and improve the generation process. This approach allows for more accurate and contextually relevant outputs, particularly in complex query scenarios. Common use cases for RAG include question answering, summarization, and conversational agents, where leveraging external information can significantly boost performance and coherence.