Generative AI Course

Comprehensive Learning Path - Hands-on course covering modern generative AI techniques and applications

---

🎯 Course Overview

Practical AI Development Training

This course transforms AI learning from theoretical concepts into practical expertise. The comprehensive curriculum covers prompt engineering, LangChain, RAG systems, and multi-agent architectures—all while building real applications and understanding industry best practices.

---

📚 Core Modules

Module	Topic	Description
M3	Prompting Strategies	Advanced prompt engineering and optimization techniques
M4	GenAI Applications	Real-world AI triage systems and interactive applications
M5	LangChain Framework	Templates, memory management, and chain composition
M6	AI Agents	Autonomous agents with tool integration and coordination
M8	RAG Systems	Retrieval-Augmented Generation with vector databases
M10	Advanced Topics	LangGraph workflows and complex AI architectures
M11	Multi-Agent Systems	CrewAI framework and investment analysis agents

🔬 Specialized Labs

• Health AI Lab - Medical applications of large language models
• API Integration - ChatGPT API mastery and custom prompt development

---

🛠️ Technology Stack

Python • OpenAI API • LangChain • LangGraph • CrewAI • Vector Databases • Jupyter Notebooks

---

🚀 Quick Start

Every lab runs in Google Colab out of the box. Open any .ipynb, click Open in Colab, and:

1. Set the API key once. In Colab → 🔑 sidebar → add a secret named exactly OPENAI_API_KEY and toggle Notebook access ON. Every lab uses the same name, so you set it once.
2. Run the first (setup) cell. It downloads utils.py, loads your key, pings OpenAI to confirm the key works (✅ / ❌), and pins a sticky lab-name pill at the top of the page so you always know which notebook you're in.
3. Continue with the lab.

Shared utilities (utils.py)

Symbol	Purpose
pretty_print(text, title, theme)	Styled HTML message boxes (themes: blue, red, yellow, green, gray)
DEFAULT_CHAT_MODEL	Default reasoning model (currently gpt-5) — change it once, everywhere
DEFAULT_MINI_MODEL	Cheaper / faster default (currently gpt-5-mini)
DEFAULT_EMBED_MODEL	Default embeddings model (currently text-embedding-3-small)
get_openai_key(verify=True)	Reads OPENAI_API_KEY from Colab secret → env var → prompt; verifies
verify_openai_key()	Standalone API-key health check
lab_pill(title)	Sticky pill banner at top of notebook

📌 Models update fast. Every lab references DEFAULT_CHAT_MODEL rather than a hardcoded model string. To upgrade the whole course to a newer LLM, change one constant in utils.py. Students are welcome (and encouraged) to swap to any newer OpenAI / Anthropic / Google model they have access to.

---

🎓 Learning Outcomes

Upon completion, you'll master:

✅ Advanced prompt engineering and optimization
✅ Building production-ready AI applications
✅ Implementing RAG systems for knowledge integration
✅ Developing autonomous AI agents
✅ Creating multi-agent collaborative systems
✅ API integration and deployment strategies

---

📁 Repository Structure

📦 generative-ai-course
├── 📓 M3_Lab1_Prompting_Strategies.ipynb
├── 📓 M4_Lab1_GenAI_Triage.ipynb
├── 📓 M4_Lab2_GenAI_BeerGame_V1.ipynb
├── 📓 M5_Lab1_LangChain_LLMs.ipynb
├── 📓 M5_Lab2_LangChain_Templates_Memory.ipynb
├── 📓 M6_Lab1_AI_Agents.ipynb
├── 📓 M6_Lab2_AI_Agents_Applications.ipynb
├── 📓 M8_Lab1_RAG.ipynb
├── 📓 M10_Lab1_LangGraph_Intro.ipynb
├── 📓 M11_Lab1_CrewAI.ipynb
├── 📓 M11_Lab2_Multi_Agent_Investment_Analysis.ipynb
├── 📓 Lab_7_HealthLLM.ipynb
├── 📓 ChatGPT_API_Tutorial.ipynb
├── 📓 Prompting_with_API.ipynb
└── 📄 README.md

---

Built for the future of AI development