Tutorial Overview
Welcome to the LazyLLM tutorials!
This series helps you get started quickly and dive deep into every LazyLLM capability.
Tutorial Catalog
From Data to LLM
- From Data to LLM The English version will be published later. You can read the Chinese version first.
LazyLLM RAG Tutorials
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Chapter 1: Understanding the Principles of RAG — Making Retrieval-Augmented Generation No Longer a Black Box
A thorough breakdown of RAG fundamentals and architecture. -
Chapter 2: Build a Minimal RAG System in 10 Minutes
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
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
Learn to evolve RAG scripts into modular, maintainable projects. -
Chapter 5: Building Custom Reader Components — Easily Parse HTML, PDF, and Other Complex Document Formats
Customize document readers to handle HTML, PDF, and more. -
Chapter 6: Retrieve with Higher Accuracy — Core Logic and Techniques for Improving RAG Recall Performance
Learn the levers behind recall quality and how to optimize them. -
Chapter 7: Retrieval Upgrade in Practice — Build a “Smarter” Document Understanding System Yourself!
Hands-on techniques to boost document understanding in practice. -
Chapter 8: Beyond Cosine — Matching Strategies Determine the Quality of Your Retrieval
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
Fine-tune embeddings and LLMs with your domain data. -
Chapter 10: Exploring Deepseek: Building a RAG system with stronger reasoning abilities
Integrate DeepSeek-class models to strengthen multi-step reasoning. -
Chapter 11: Performance Optimization Guide: From cold start to responsive acceleration of your RAG
End-to-end techniques for faster cold start and response latency. -
Chapter 12: Practice: Accelerate your RAG with caching, asynchronous and vector engines
Accelerate RAG with caching, async workflows, and vector engines. -
Chapter 13: RAG + Multimodal — A Q&A System That Handles Images and Tables Alike
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
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?
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
Add macro summaries and chart generation for richer paper QA. -
Chapter 17: a full-link solution for permissions, sharing and content security
Full-stack enterprise practices for access control and content safety. -
Chapter 18 High-level RAG: Agentic RAG
Blend agents with RAG to orchestrate stronger task execution. -
Chapter 19: Advanced RAG — Knowledge-Graph-Based RAG
Fuse knowledge graphs with generative QA for precise recall.
Ready to explore? Click any lecture to dive in 🚀