Retrieval-Augmented Generation (RAG) Tutorial

A comprehensive, hands-on tutorial that takes you from the basics through to building production-ready RAG systems.

🎯 What You'll Learn

By the end of this tutorial, you'll understand:

• What RAG is and how it contributes to AI applications
• How vector embeddings capture semantic meaning
• How to build and query vector databases
• How to integrate retrieval with language models
• Advanced techniques for production RAG systems

🚀 Who This Is For

• Developers: Learn to build RAG applications
• ML Engineers: Understand the architecture and implementation details
• Product Managers: Gain technical insight into RAG capabilities

📚 Learning Path

Part 1: Foundations

1. Introduction to RAG

   • The problem RAG solves
   • How RAG works at a high level
   • Real-world applications

2. Understanding Embeddings

   • What are vector embeddings?
   • Semantic similarity
   • Embedding models

3. Vector Databases & Retrieval

   • Storing and indexing vectors
   • Similarity search algorithms
   • Retrieval strategies

4. Language Models & Generation

   • How LLMs work
   • Prompting techniques
   • Integrating retrieved context

Part 2: Building RAG Systems

5. Building Your First RAG System

   • Step-by-step implementation
   • Working code example
   • Testing and evaluation

6. Advanced RAG Techniques

   • Document chunking strategies
   • Re-ranking retrieved results
   • Hybrid search
   • Query optimization
   • Handling edge cases

Part 3: Practical Examples

• Example 1: Simple Question Answering
• Example 2: Document Chat System
• Example 3: Advanced RAG Pipeline

🛠️ Setup

Prerequisites

• Python 3.13 (or 3.10+)
• Basic Python knowledge
• Understanding of APIs (helpful but not required)

Installation

1. Clone this repository:

git clone <repository-url>
cd myproject

2. Create and activate virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up API keys (you'll need at least one):

# Create a .env file
cp .env.example .env

# Add your API keys:
# OPENAI_API_KEY=your-key-here
# Or for free alternatives:
# HUGGINGFACE_API_KEY=your-key-here

📖 How to Use This Tutorial

1. Start with Lesson 1 and work through sequentially
2. Read the theory in each lesson markdown file
3. Run the code examples to see concepts in action
4. Experiment with the examples and modify them
5. Build your own RAG system for your use case

🎓 Learning Tips

• Take your time: RAG combines multiple complex concepts
• Run every code example: Hands-on practice is crucial
• Experiment: Modify parameters and see what happens
• Build projects: Apply what you learn to real problems
• Ask questions: Open issues if something isn't clear

📊 What is RAG?

Retrieval-Augmented Generation is a technique that enhances large language models by giving them access to external knowledge. Instead of relying solely on their training data, RAG systems:

1. Retrieve relevant information from a knowledge base
2. Augment the LLM's prompt with this information
3. Generate accurate, grounded responses

This solves key problems like:

• ✅ Hallucinations (making up facts)
• ✅ Outdated information
• ✅ Lack of domain-specific knowledge
• ✅ Inability to cite sources

🌟 Why RAG Matters

RAG has become the standard approach for:

• Customer support chatbots: Answer from company documentation
• Research assistants: Search through papers and documents
• Enterprise search: Query internal knowledge bases
• Personal assistants: Access your notes and files
• Legal/Medical AI: Ground responses in verified sources

🔧 Technology Stack

This tutorial uses:

• Python: Primary programming language
• OpenAI/Anthropic APIs: For embeddings and LLMs (with free alternatives)
• ChromaDB: Vector database (simple, local, no setup)
• LangChain (optional): Framework for building LLM apps
• Sentence Transformers: Free, open-source embeddings

📁 Repository Structure

rag_tutorial/
├── README.md                          # You are here
├── lessons/                           # Step-by-step lessons
│   ├── 01-introduction-to-rag.md
│   ├── 02-understanding-embeddings.md
│   ├── 03-vector-databases-retrieval.md
│   ├── 04-language-models-generation.md
│   ├── 05-building-simple-rag.md
│   └── 06-advanced-rag-techniques.md
├── examples/                          # Working code examples
│   ├── 01-simple-qa/
│   ├── 02-document-chat/
│   └── 03-advanced-rag/
├── notebooks/                         # Jupyter notebooks for exploration
│   ├── embeddings-exploration.ipynb
│   └── rag-from-scratch.ipynb
├── utils/                            # Helper utilities
│   ├── embeddings.py
│   ├── retrieval.py
│   └── generation.py
├── data/                             # Sample data for examples
├── requirements.txt                  # Python dependencies
└── .env.example                      # Environment variables template

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Ready to start? Head to Lesson 1: Introduction to RAG