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 …

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating an information-retrieval mechanism that allows models to access and utilize additional data …

Oct 8, 2025 · Retrieval-Augmented Generation (RAG) is an advanced AI framework that combines information retrieval with text generation models like GPT to produce more accurate …

Nov 12, 2024 · Traditional RAG models typically retrieve isolated facts from unstructured data sources. In contrast, Graph RAG leverages structured data from knowledge graphs, enabling it …

RAG (Retrieval-Augmented Generation) is an AI framework that combines the strengths of traditional information retrieval systems (such as search and databases) with the capabilities …

Jan 31, 2025 · Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.

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 …

Dec 9, 2025 · Traditional RAG vs Agentic RAG explained in depth. Learn the differences, use cases, benefits, and why Agentic RAG is the future of intelligent search systems.

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 …

Readme for classic RAG in Azure AI Search This series demonstrates the classic pattern for building RAG solutions on Azure AI Search. Classic RAG uses the original search engine …