# Chapter 7: Retrieval-Augmented Generation (RAG)

> 📚 *"RAG is the most practical solution to the knowledge limitations of LLMs — it lets Agents 'consult' external knowledge bases and give well-grounded answers."*

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## Chapter Overview

RAG (Retrieval-Augmented Generation) is one of the most important AI application technologies today. LLMs have a knowledge cutoff date and cannot access your private data. RAG solves this by "retrieving first, then generating", allowing Agents to answer questions based on the latest, domain-specific knowledge. This chapter covers everything from principles to hands-on practice for building RAG systems.

## Chapter Goals

After completing this chapter, you will be able to:

- ✅ Understand the core principles and workflow of RAG
- ✅ Master best practices for document loading and text splitting
- ✅ Use vector embeddings and vector databases for semantic retrieval
- ✅ Apply hybrid retrieval, reranking, and other strategies to improve retrieval quality
- ✅ Build a complete intelligent document Q&A Agent

## Chapter Structure

| Section | Content | Difficulty |
|---------|---------|-----------|
| 7.1 RAG Concepts and How It Works | Why do we need RAG? How does it work? | ⭐⭐ |
| 7.2 Document Loading and Text Splitting | Processing documents in various formats | ⭐⭐ |
| 7.3 Vector Embeddings and Vector Databases | Semantic storage and retrieval | ⭐⭐⭐ |
| 7.4 Retrieval Strategies and Reranking | Improving retrieval precision | ⭐⭐⭐ |
| 7.5 Practice: Intelligent Document Q&A Agent | Complete system implementation | ⭐⭐⭐⭐ |

## ⏱️ Estimated Study Time

Approximately **90–120 minutes** (including hands-on exercises)

## 💡 Prerequisites

- Completed the vector database fundamentals in Chapter 5 (Memory Systems)
- Familiar with Python file operations and HTTP requests
- Basic familiarity with the OpenAI Embeddings API

## 🔗 Learning Path

> **Prerequisites**: [Chapter 5: Memory Systems](../chapter_memory/README.md) (especially the vector database section)
>
> **Recommended Next Steps**:
> - 👉 [Chapter 8: Context Engineering](../chapter_context_engineering/README.md) — Systematically manage context retrieved by RAG
> - 👉 [Chapter 21: AI Coding Assistant](../chapter_coding_agent/README.md) — Apply RAG for code search in a real project

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*Next: [7.1 RAG Concepts and How It Works](./01_rag_concepts.md)*
