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 …

What is Retrieval-Augmented Generation? Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base …

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 and up-to-date …

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 …

Dec 15, 2025 · Retrieval-augmented Generation (RAG) is a pattern that extends LLM capabilities by grounding responses in your proprietary content. While conceptually simple, RAG implementations …

Jan 31, 2025 · 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 …

Dec 2, 2025 · Retrieval-augmented generation (RAG) is a technique that improves the quality and relevance of answers from a generative AI application. It works by linking the pre-trained knowledge …

Retrieval-Augmented Generation (RAG) is an emerging approach that enhances language models by integrating document retrieval into the generation process.

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

Oct 3, 2024 · RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs, addressing key limitations of LLMs. The study explores the basic architecture of …