🚀 10-Day RAG Beginner Roadmap

Welcome to your comprehensive learning journey into Retrieval-Augmented Generation (RAG)! This repository is designed for absolute beginners who want to master RAG from the ground up in just 10 days.

📖 Description

This roadmap takes you from Python fundamentals all the way to building and deploying a complete RAG application. Each day builds upon the previous one, ensuring you have a solid foundation before moving to more advanced concepts. By the end of 10 days, you'll have hands-on experience with:

• Python programming for AI applications
• Large Language Models (LLMs) and their capabilities
• Prompt engineering techniques
• Data extraction and chunking strategies
• Vector embeddings and databases
• Building RAG systems from scratch
• Using frameworks like LangChain and LlamaIndex
• Advanced RAG techniques
• Deploying production-ready RAG applications

🎯 How to Use This Repository

1. Study Day-by-Day: Follow the roadmap sequentially, starting with Day 1
2. Read the Notes: Open each day's folder and read the README.md file thoroughly
3. Complete Assignments: Work through the assignment.md file for hands-on practice
4. Practice Regularly: Code along with the examples and complete all practice tasks
5. Build Projects: Each day includes a mini-project to reinforce your learning

Recommended Study Schedule

• Time per day: 2-4 hours
• Read notes: 30-60 minutes
• Complete assignments: 1-2 hours
• Mini project: 30-60 minutes

🛠️ Technical Requirements

Python Version

• Python 3.8 or higher (Python 3.10+ recommended)

Required Libraries

You'll install these progressively throughout the roadmap:

# Core libraries
pip install openai
pip install langchain
pip install llama-index
pip install chromadb
pip install sentence-transformers
pip install pypdf
pip install beautifulsoup4
pip install requests
pip install fastapi
pip install streamlit
pip install uvicorn

API Keys

You'll need API keys for certain days:

• OpenAI API Key (for Days 2, 3, 6, 7, 8, 9, 10)
  • Sign up at platform.openai.com
  • Get your API key from the API keys section
  • Store it securely (use environment variables)

Environment Setup

Create a .env file in the root directory:

OPENAI_API_KEY=your_api_key_here

📚 Roadmap Overview

Day	Topic	Focus Area
Day 1	Python Foundations for GenAI	Python basics, data structures, file handling, APIs
Day 2	Generative AI & LLM Basics	Understanding LLMs, OpenAI API, model capabilities
Day 3	Prompt Engineering Essentials	Crafting effective prompts, few-shot learning, chain-of-thought
Day 4	Chunking & Data Extraction	PDF parsing, web scraping, document processing
Day 5	Embeddings & Vector Databases	Vector embeddings, similarity search, ChromaDB
Day 6	RAG Fundamentals	Retrieval → Augmentation → Generation pipeline
Day 7	Implement RAG From Scratch	Building RAG system with pure Python
Day 8	RAG Using LangChain or LlamaIndex	Using popular RAG frameworks
Day 9	Advanced RAG	Reranking, query rewriting, fusion techniques
Day 10	Build & Deploy RAG Application	FastAPI/Streamlit deployment, production considerations

🗂️ Repository Structure

rag-roadmap/
│
├── Day01/
│   ├── README.md
│   └── assignment.md
│
├── Day02/
│   ├── README.md
│   └── assignment.md
│
├── Day03/
│   ├── README.md
│   └── assignment.md
│
├── Day04/
│   ├── README.md
│   └── assignment.md
│
├── Day05/
│   ├── README.md
│   └── assignment.md
│
├── Day06/
│   ├── README.md
│   └── assignment.md
│
├── Day07/
│   ├── README.md
│   └── assignment.md
│
├── Day08/
│   ├── README.md
│   └── assignment.md
│
├── Day09/
│   ├── README.md
│   └── assignment.md
│
├── Day10/
│   ├── README.md
│   └── assignment.md
│
└── README.md   (this file)

💡 Learning Tips

1. Don't Skip Days: Each day builds on previous concepts
2. Code Along: Type out the examples yourself, don't just read
3. Experiment: Modify examples to see what happens
4. Ask Questions: If something is unclear, research it
5. Take Notes: Write down key concepts in your own words
6. Build Projects: The mini-projects are crucial for understanding

🎓 Prerequisites

• Basic understanding of programming concepts (variables, functions, loops)
• Familiarity with command line/terminal
• Willingness to learn and experiment
• No prior AI/ML experience required!

📝 Notes

• All code examples are beginner-friendly
• Solutions are not provided for assignments (learning by doing!)
• You can work at your own pace, but try to complete one day per day
• Feel free to revisit previous days if needed

🙏 Credits

Created by Chandra Sekhar

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Ready to start? Navigate to Day01/ and begin your RAG journey! 🚀