Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation is a methodology used in the field of Generative AI. It combines the benefits of both retrieval-based and generative systems for language processing. RAG utilizes an external knowledge source to retrieve relevant documents or information, and then uses a generative model to create a contextually appropriate response or output. This approach enhances the model's ability to generate detailed, accurate, and context-specific responses, making it particularly useful in applications such as chatbots, question-answering systems, and content creation tools.