RAG Full-Stack Development
This course is for developers with some AI and programming background, focusing on the principles, system composition, security considerations, and implementation of Retrieval-Augmented Generation (RAG). You will systematically learn the working mechanism of LLM and RAG, and master the complete E2E project construction method, including key modules such as index construction, query optimization, and permission control. The course adopts a combination of theory and practice to help you develop the ability to independently develop and deploy enterprise-level intelligent question-answering systems.
Day 1: RAG Theory and Core Process
Systematic Understanding from Concept to Technical Architecture
Study Content +
1.1 RAG Overview
What is RAG? (What)
Why is RAG needed? (Why)
Analysis of typical application scenarios
Introduce the overall technical architecture of RAG in combination with LangChain/LangGraph
1.2 Two-Stage Workflow of RAG
Overview of the core working mechanism of the RAG system
Analysis of the two-stage collaborative mode
๐ฅ Indexing Stage
๐ Retrieving Stage
1.3 Tool Practice Demonstration
Use LangChain/LCEL to demonstrate the complete RAG process: document loading โ Embedding โ retrieval
Operate a vector database (taking pgvector as an example)
Learning Objectives +
- โ In-depth understanding of the working principle and value of the RAG system
- โ Master the complete document processing and vectorization process
- โ Proficient in using mainstream vector databases and retrieval components
- โ Possess the ability to design the architecture of a RAG system
Day 2: RAG Practice and Optimization
Complete Implementation from Experiment to Production
Study Content +
2.1 Practice Preparation
Technology stack description: Front-end: Next.js + Tailwind CSS, Back-end: LangChain / LCEL + PostgreSQL + pgvector
Introduction to practice objectives and business background
2.2 Experiment 1: Indexing Pipeline Implementation
Implement the complete document processing flow: loading โ chunking โ vectorization โ storage
Programming demonstration in combination with LangChain / LCEL
2.3 Experiment 2: Retrieving Pipeline Implementation
Build a complete link from retrieval to generation: retrieve documents โ assemble Prompt โ generate LLM โ return results
Support multiple retrieval methods: vector retrieval, hybrid retrieval
2.4 Experiment 3: Query Optimization and Enhancement
Introduce hybrid retrieval and reranking to improve retrieval effectiveness
Practice the CAG (Cache-Augmented Generation) solution
Optimize response quality and speed for specific business scenarios
2.5 Experiment 4: Permission Control and Security Design
The role and design method of Metadata
Practice of document permission control based on user/organization
Build a secure and reliable retrieval process
2.6 Summary and Q&A
Review of core knowledge of the course
Frequently asked questions
Recommendations for subsequent advanced learning resources
Learning Objectives +
- โ Independently build a complete RAG system prototype
- โ Master multiple query optimization techniques and parameter tuning methods
- โ Implement enterprise-level permission control and security isolation
- โ Possess system performance tuning and monitoring capabilities
- โ Proficient in using the LangChain ecosystem for engineering development