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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