Many practitioners have found that although RAG can quickly build a demo in a short time, it faces numerous challenges in actual production environments. This article analyzes the core issue of RAG’s industrial implementation from the perspective of entrepreneurs in the AI large model field—problem grading—and discusses the challenges and solutions of four types of … Read more
Happy New Year, everyone! Today, I will interpret Baichuan’s RAG approach. Baichuan Intelligent has a profound background in search; let’s see how they navigated the pitfalls of RAG! In general, Baichuan combines a long context model (192k) with search enhancement methods to address knowledge updates and reduce model hallucinations, achieving 95% accuracy on a dataset … Read more
Reprinted from WeChat Official Account | Blue’s Little Firefly In this article, I would like to accurately introduce the application areas of Knowledge Graphs (KG) in the RAG pipeline. We will explore the different types of questions that arise in the RAG pipeline and how to address these issues by applying knowledge graphs at various … Read more
Abstract: The transition from traditional RAG to Graph RAG enhances large language models by integrating knowledge graphs, enabling them to provide more detailed and accurate responses to complex queries. The effectiveness of Graph RAG also depends on the quality and breadth of the underlying knowledge graph and the engineering aspects of RAG. Main Points: – … Read more
Wang Haofen, Tongji University, “Hundred Talents Program”, Distinguished Researcher, PhD Supervisor Personal Introduction: Wang Haofen, distinguished researcher and PhD supervisor in the “Hundred Talents Program” at Tongji University. He has served as CTO in frontline artificial intelligence companies for a long time. He is one of the initiators of OpenKG, the world’s largest Chinese open … Read more
Original: https://zhuanlan.zhihu.com/p/682253496 Organizer: Qingke AI 1. Background Introduction The RAG (Retrieval Augmented Generation) method refers to a combination of retrieval-based models and generative models to improve the quality and relevance of generated text. This method was proposed by Meta in the 2020 paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”[1], allowing language models (LM) to acquire … Read more
Click the blue text above to follow us 1. Introduction In the wave of artificial intelligence, Retrieval-Augmented Generation (RAG) technology has become a hot topic in research and application due to its unique advantages. RAG technology combines the powerful generative capabilities of large language models (LLMs) with efficient information retrieval systems, providing users with a … Read more
1. Introduction Since leaving the last company with Yuanwai, we started our own company focusing on the development of RAG large model AI product applications. During this period, which included a Spring Festival, the total time was about three months. We worked day and night, and as of the end of March, the product has … Read more
“ Happy New Year! This article should be the last piece in the collection of algorithm insights regarding RAG. Throughout the past year, most of my work has focused on some aspects of large model applications, and I would like to briefly discuss two points that left a significant impression on me regarding RAG system … Read more
Many people say that 2025 will be the year of intelligent agents, and in 2024, many efficiency-seeking programmers have already started using Cursor/Windsurf. It seems to take us a step further than GitHub Copilot from a few years ago. I often ponder what programming will look like in the future and how Cursor and LLMs … Read more