datawhalechina/all-in-rag
🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/
This is a comprehensive guide for AI engineers and product developers to build advanced question-answering and knowledge retrieval systems using Large Language Models (LLMs). It takes you from understanding foundational RAG concepts to building production-ready applications, processing various data inputs, and producing highly accurate, contextually relevant answers. The guide targets individuals keen on developing smart information retrieval tools and interactive AI experiences.
4,659 stars. Actively maintained with 1 commit in the last 30 days.
Use this if you are an AI engineer or product developer looking to master Retrieval Augmented Generation (RAG) to build robust, intelligent question-answering and knowledge retrieval systems for your specific domain.
Not ideal if you are looking for a simple, plug-and-play RAG solution without diving into the underlying theory, data processing, and system optimization aspects.
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Mar 06, 2026
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