In this article, we will explore how Agentic RAG helps to address the limitations of traditional RAG. RAG Framework The RAG (Retrieval-Augmented Generation) framework operates in a specific sequence: Document -> Document Fragments -> Vector Database -> Fragment Retrieval (Top K) -> Large Language Model (LLM) However, this order encounters obstacles when handling certain types … Read more
Hello everyone! Welcome to a channel focused on AI agents~ Today, I’m sharing a 35-page latest overview of Agentic RAG! There are a lot of pictures, which I believe many of you will enjoy. 1. Why Do We Need Agentic RAG? Although traditional LLMs are powerful, they are limited by static training data and often … Read more
In 2023, Retrieval-Augmented Generation (RAG) technology dominated the landscape, and in 2024, agentic workflows are driving significant advancements. The use of AI agents opens up new possibilities for building more powerful, robust, and versatile applications powered by large language models (LLMs). One potential use case is to enhance AI agents within the agentic RAG process. … Read more
Despite Retrieval-Augmented Generation (RAG) dominating in 2023, agentic workflows bring significant advancements in 2024. The application of AI agents opens up new possibilities for developing more powerful, robust, and versatile applications driven by large language models (LLMs). One possibility is to leverage AI agents in agentic RAG pipelines to enhance RAG processes. This article will … Read more
Figure: Agentic RAG (Image from the author) 1. Introduction Given the way technology evolves, we have become accustomed to thinking about certain solution constructs from the perspective of specific tools and platforms. For example, Retrieval-Augmented Generation (RAG) is ubiquitous in today’s Gen AI world. Given its emergence in the Gen AI era, we mainly view … Read more