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Retrieval-augmented generation - Wikipedia Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating an information-retrieval mechanism that allows models to access and utilize additional data beyond their original training set
What is RAG? - Retrieval-Augmented Generation AI Explained - AWS Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response
What is Retrieval-Augmented Generation (RAG) - GeeksforGeeks Retrieval-Augmented Generation (RAG) is an advanced AI framework that combines information retrieval with text generation models like GPT to produce more accurate and up-to-date responses
Simple RAG Explained: A Beginner’s Guide to Retrieval-Augmented . . . RAG stands for Retrieval-Augmented Generation Think of it as giving your AI a specific relevant documents (or chunks) that it can quickly scan through to find relevant information before answering your questions
What is retrieval augmented generation (RAG)? - IBM Retrieval augmented generation, or RAG, is an architecture for optimizing the performance of an artificial intelligence (AI) model by connecting it with external knowledge bases RAG helps large language models (LLMs) deliver more relevant responses at a higher quality
What is RAG? | Microsoft Azure Learn about retrieval-augmented generation (RAG), an AI framework that combines retrieval-based and generative models to produce more accurate responses