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 (RAG) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. [1] It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information …

2024年12月18日 · Learn how generative AI and retrieval augmented generation (RAG) patterns are used in Azure AI Search solutions.

2025年2月10日 · Retrieval-Augmented Generation (RAG) systems represent a significant leap forward in the realm of Generative AI, seamlessly integrating the capabilities of information retrieval and text generation. Unlike traditional models like GPT, which predict the next word based solely on previous context, RAG

2024年10月21日 · Retrieval augmented generation (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.

2025年1月31日 · Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.

4 天之前 · This article talks about the importance and need for Retrieval Augmented Generation (RAG) and index in generative AI. What is RAG? Some basics first. Large language models (LLMs) like ChatGPT are trained on public internet data that was available at the point in time when they were trained.

4 天之前 · Retrieval-Augmented Generation (RAG) is an advanced AI framework that combines retrieval-based learning with generative AI models to enhance the accuracy and relevance of AI-generated content. Unlike traditional generative AI models that rely solely on their pre-trained knowledge, RAG AI retrieves relevant data from external sources such as:

2025年2月13日 · It is an AI framework that merges the advantages of traditional information retrieval systems (such as search engines and databases) with the capabilities of generative models, like Large Language Models (LLMs). Think of RAG as a hybrid model that utilizes both parametric and non-parametric memory.

2025年3月28日 · Read more about AI, LLMs and RAG. Why run AI on-premise? Much of artificial intelligence’s rapid growth has come from cloud-based tools. But there are very good reasons to host AI workloads on ...