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) 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 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 Why is Retrieval-Augmented Generation important?
What is retrieval-augmented generation (RAG)? Traditional RAG models typically retrieve isolated facts from unstructured data sources In contrast, Graph RAG leverages structured data from knowledge graphs, enabling it to handle complex queries more effectively
An introduction to RAG and simple complex RAG - Medium We discuss what RAG is, the trade-offs between RAG and fine-tuning, and the difference between simple naive and complex RAG, and help you figure out if your use-case may lean more heavily
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