Technical Guide
6.1k 2026-04-13
datawhalechina/all-in-rag
A comprehensive, full-stack guide to Retrieval-Augmented Generation (RAG) technology, covering theory, practice, and engineering best practices for building LLM applications.
Core Features
Systematic learning path from basics to advanced RAG
Combines theoretical knowledge with hands-on coding practice
Supports multimodal RAG, including text and image retrieval
Focuses on engineering best practices for production readiness
Includes rich practical projects for skill consolidation
Detailed Introduction
This project is a comprehensive, full-stack tutorial for developers building RAG (Retrieval-Augmented Generation) applications with large language models. It aims to provide a structured learning path and hands-on projects, enabling developers to master RAG application development, from core concepts and data processing to advanced retrieval, indexing optimization, and system evaluation. It addresses the lack of systematic RAG resources by offering a complete learning system for building production-grade intelligent Q&A and knowledge retrieval systems.