Skip to content

Latest commit

 

History

History
115 lines (78 loc) · 3.47 KB

README.md

File metadata and controls

115 lines (78 loc) · 3.47 KB

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.

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