LLM Engineering Discovery

This repository contains my personal learning journey and explorations based on Ed Donner's "Mastering LLM Engineering" course.

🎓 Course Repository: https://github.com/ed-donner/llm_engineering

📖 About This Repository

This is a personal discovery and learning repository where I explore concepts, complete assignments, and experiment with Large Language Model engineering techniques from the comprehensive 8-week course by Ed Donner.

Course Overview

The original course covers:

• Week 1: Foundations & Setup
• Week 2: Core LLM Concepts
• Week 3: Advanced Techniques
• Week 4: Vector Databases & RAG
• Week 5: LLM Agents & Function Calling
• Week 6: Multi-Agent Systems
• Week 7: Production & Deployment
• Week 8: Advanced Topics & Capstone

📁 Repository Structure

• assignments/ – Final versions of assignment notebooks
• notebooks/ – Exploratory work and research notes
• prompts/ – Saved prompt templates and variations
• data/ – Input/output data files and datasets
• experiments/ – Personal experiments and proof-of-concepts

🛠️ Setup

Prerequisites

• Python 3.11+
• Git
• API keys for various LLM providers (OpenAI, Anthropic, etc.)

1. Clone and Setup Environment

# Clone this repository
git clone <your-repo-url>
cd llm_engineering_discovery

# Create Python environment using Anaconda
conda create -n llm-engineering python=3.11
conda activate llm-engineering

# Install dependencies
pip install -r requirements.txt

2. Environment Configuration

Copy the .env.example file to .env and configure your API keys:

cp .env.example .env
# Edit .env with your actual API keys

Required environment variables:

• OPENAI_API_KEY - OpenAI API key
• ANTHROPIC_API_KEY - Anthropic API key
• GOOGLE_API_KEY - Google AI API key
• Other provider keys as needed

3. Start Jupyter Lab

jupyter lab

🔧 Tools & Technologies

This repository utilizes the following key technologies from the course:

• LLM Providers: OpenAI, Anthropic, Google AI, Ollama
• Frameworks: LangChain, LlamaIndex, Transformers
• Vector Databases: ChromaDB, Pinecone
• Development: Jupyter Lab, Python, Git
• Deployment: Modal, Gradio, Streamlit

📚 Learning Resources

• Original Course: ed-donner/llm_engineering
• Setup Guides: Available for Mac, PC, and Linux in the original repo
• Community: Course Discord and community contributions
• Slides & Resources: Course materials and presentations

🎯 Learning Objectives

Through this repository, I aim to:

• Master fundamental LLM engineering concepts
• Build practical applications using LLMs
• Understand RAG (Retrieval-Augmented Generation) systems
• Develop multi-agent systems
• Learn production deployment strategies
• Explore cutting-edge LLM techniques

🙏 Acknowledgments

Special thanks to Ed Donner for creating this comprehensive LLM Engineering course. This repository is built upon the excellent foundation and curriculum provided in his course.

📄 License

This repository is for educational purposes. Please refer to the original course repository for licensing information regarding course materials.

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This is a personal learning repository based on Ed Donner's LLM Engineering course. For the official course materials, please visit the original repository.