Future Tech Feed

What is Retrieval-Augmented Generation? Retrieval-augmented generation (RAG) is a technique used in natural language processing that combines retrieval-based models and generative models to enhance the quality and relevance of generated text. RAG is an AI framework that retrieves data from external sources of knowledge to improve the accuracy and reliability of responses from large language models (LLMs). How does it work? RAG combines the strengths of both retrieval and generative AI. Retrieval-based models are excellent at extracting information from pre-existing sources but cannot produce original responses. On the other hand, generative models can generate original and contextually appropriate responses but may struggle with accuracy and factual correctness. RAG integrates these two approaches, using a retrieval model to find relevant information, which is then used as input for the generative model. This allows the generative model to leverage the accuracy of the retrieval mode...