# Tutorial Overview

Welcome to the LazyLLM tutorials!

This series helps you get started quickly and dive deep into every LazyLLM capability.

## Tutorial Catalog

- [Chapter 1: Understanding the Principles of RAG — Making Retrieval-Augmented Generation No Longer a Black Box](1.md)  
  A thorough breakdown of RAG fundamentals and architecture.

- [Chapter 2: Build a Minimal RAG System in 10 Minutes](2.md)  
  Build a minimal but working RAG system from scratch in minutes.

- [Chapter 3: How Large Models Work — Understanding Call Logic and Prompt Engineering with LazyLLM](3.md)  
  Understand how LLM calls flow and how to structure prompts effectively.

- [Chapter 4: Engineering Your First RAG Project — From Scripts to Modular and Maintainable Systems](4.md)  
  Learn to evolve RAG scripts into modular, maintainable projects.

- [Chapter 5: Building Custom Reader Components — Easily Parse HTML, PDF, and Other Complex Document Formats](5.md)  
  Customize document readers to handle HTML, PDF, and more.

- [Chapter 6: Retrieve with Higher Accuracy — Core Logic and Techniques for Improving RAG Recall Performance](6.md)  
  Learn the levers behind recall quality and how to optimize them.

- [Chapter 7: Retrieval Upgrade in Practice — Build a “Smarter” Document Understanding System Yourself!](7.md)  
  Hands-on techniques to boost document understanding in practice.

- [Chapter 8: Beyond Cosine — Matching Strategies Determine the Quality of Your Retrieval](8.md)  
  Compare vector matching strategies beyond cosine and when to use each.

- [Chapter 9: Fine-Tuning in Practice — Help Large Models and Embedding Models Better Understand Your Domain](9.md)  
  Fine-tune embeddings and LLMs with your domain data.

- [Chapter 10: Exploring Deepseek: Building a RAG system with stronger reasoning abilities](10.md)  
  Integrate DeepSeek-class models to strengthen multi-step reasoning.

- [Chapter 11: Performance Optimization Guide: From cold start to responsive acceleration of your RAG](11.md)  
  End-to-end techniques for faster cold start and response latency.

- [Chapter 12: Practice: Accelerate your RAG with caching, asynchronous and vector engines](12.md)  
  Accelerate RAG with caching, async workflows, and vector engines.

- [Chapter 13: RAG + Multimodal — A Q&A System That Handles Images and Tables Alike](13.md)  
  Build multimodal RAG that digests images, tables, and text together.

- [Chapter 14: Practical Session - Building a RAG System That Supports Complex Academic Paper Question Answering](14.md)  
  Craft paper-focused QA with structure-aware, context-rich retrieval.

- [Chapter 15: Big Perspective Q&A: How does RAG support cross-document and cross-dimensional summarization?](15.md)  
  Support cross-document summaries and multi-dimensional QA.

- [Chapter 16: Create a RAG system for paper Q&A with macro Q&A and chart generation functions](16.md)  
  Add macro summaries and chart generation for richer paper QA.

- [Chapter 17: a full-link solution for permissions, sharing and content security](17.md)  
  Full-stack enterprise practices for access control and content safety.

- [Chapter 18 High-level RAG: Agentic RAG](18.md)  
  Blend agents with RAG to orchestrate stronger task execution.

- [Chapter 19: Advanced RAG — Knowledge-Graph-Based RAG](19.md)  
  Fuse knowledge graphs with generative QA for precise recall.

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