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
Retrieval-augmented generation - Wikipedia Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information [1] With RAG, LLMs do not respond to user queries until they refer to a specified set of documents
What is Retrieval-Augmented Generation (RAG) - GeeksforGeeks Retrieval-augmented generation (RAG) is an innovative approach in the field of natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models to enhance the quality of generated text
What is retrieval-augmented generation (RAG)? RAG is a method that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs It works by: Retrieval: When a user query is received, the system searches a large, up-to-date database or corpus for relevant documents
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
What Is Retrieval-Augmented Generation, aka RAG? - NVIDIA Blog So, What Is Retrieval-Augmented Generation (RAG)? Retrieval-augmented generation is a technique for enhancing the accuracy and reliability of generative AI models with information fetched from specific and relevant data sources