Author：Deephub Imba This article is approximately 2800 words, recommended reading time 5minutes This article introduces how the diffusion model works. The rise of diffusion models can be seen as a major factor in the recent breakthroughs in the field of AI-generated art. The development of the stable diffusion model allows us to easily create wonderful … Read more
RAG 75% for retrieval, 25% for generation. All aimed at enhancing the ability to retrieve relevant contextual information. 1. RAG Working Principle and Technical Architecture RAG (Retrieval-Augmented Generation) is a technical architecture that combines information retrieval and generation models. Its basic working principle is to enhance the output of the generation model through the retrieval … Read more
Large Language Models (LLMs) have made significant progress in the field of generative artificial intelligence, but they face the “hallucination” problem, which is the tendency to generate inaccurate or irrelevant information. This issue is particularly severe in high-risk applications such as medical assessments and insurance claims processing. To address this challenge, researchers from the University … Read more
What is RAG Technology RAG, or Retrieval-Augmented Generation technology, is an AI architecture that combines retrieval and generation. It enhances the output quality of language models by retrieving external knowledge. This is akin to equipping large language models with an intelligent knowledge base, enabling them to retrieve and reference relevant information in real-time while generating … Read more
Roaming RAG, as an innovative RAG technology, can be elaborated on in detail regarding its characteristics and advantages from the following aspects: Working Principle and Process The core of Roaming RAG lies in utilizing the hierarchical structure of documents to enhance the information retrieval capabilities of large language models (LLMs). The specific process includes: Document … Read more
The RAG technology introduces external knowledge retrieval in the AI generation process, evolving from basic document queries to intelligent architectures with multi-modal and Multi-Agent collaborations, allowing AI to provide more accurate and comprehensive answers. Core Components: Embedding Model: Converts text into vector representations. Generation Model: Responsible for the final content generation. Re-ranking Model: Optimizes the … Read more
Are you still troubled by the mixed quality of AI in China and its poor performance? Then let’s take a look at Developer Cat AI (3in1)! This is an integrated AI assistant that combines GPT-4, Claude3, and Gemini. It covers all models of the three AI tools. Including GPT-4o and Gemini flash Now you can … Read more
Click 👇🏻 to follow, article from “ With the expanding application of large language models (LLMs), Retrieval-Augmented Generation (RAG) has become a mature technology. The popularity of products like txt2sql and ChatBI highlights the increasing importance of query construction techniques. This article analyzes the process of query construction and illustrates, through examples, how to transform … Read more
LLM (Large Language Model) is a powerful new platform, but they are not always trained on data that is relevant to our tasks or the most recent data. RAG (Retrieval Augmented Generation) is a general method that connects LLMs with external data sources (such as private data or the latest data). It allows LLMs to … Read more
Source: Deep Learning and Large Models (LLM) This article is approximately 3500 words long and is recommended for a 9-minute read. This article delves into the development of Retrieval-Augmented Generation (RAG), from basic concepts to the latest technologies. 4. Overview of Existing RAG Frameworks Agent-Based RAG A new agent-based Retrieval-Augmented Generation (RAG) framework adopts a … Read more