AI Course Examples Repository

Welcome to the Focused AI Course - a comprehensive collection of practical examples and implementations for building AI-driven applications using modern Python tools and frameworks.

🚀 Overview

Practical examples covering key AI development concepts:

• Prompt Engineering: Optimizing AI prompts with COSTAR framework
• Chatbot Development: LangGraph-based conversational AI
• RAG Applications: Document processing with web search
• Audio Processing: Transcription and summarization
• Evaluation: Model testing and performance measurement

🛠 Tech Stack

• Python 3.11+ with uv package manager
• LangChain & LangGraph for AI applications
• OpenAI GPT-4o for language models
• LangSmith for monitoring and evaluation

📋 Prerequisites

API keys needed:

• OpenAI - GPT models
• Tavily - Web search (free tier)
• LangSmith - Monitoring
• Deepgram - Audio transcription

🔧 Setup

1. Install uv: pip install uv (or see uv docs)
2. Install dependencies: uv sync
3. Configure API keys: Copy .env.sample to .env and add your keys

🏃‍♂️ Usage

Run examples with: uv run python src/module/script.py

📁 Project Structure

src/
├── chatbot/             # LangGraph conversational AI
├── evaluation/          # Model evaluation and testing
└── prompt_engineering/  # Prompt optimization

📚 Examples

# Prompt engineering with web search
uv run python src/prompt_engineering/national_parks.py

# Interactive chatbot
uv run python src/chatbot/conversation_bot.py

# Evaluation examples
uv run python src/evaluation/evaluators/eval_summaries.py

🎓 Learning Path

1. Prompt Engineering - Effective prompting techniques
2. Chatbots - Conversational AI with LangGraph
3. Evaluation - Testing and measuring AI performance
4. RAG Applications - Document-based Q&A systems