Introduction to Generative AI Concepts

Understanding Generative AI

• What is Intelligence?
• Mechanisms of Intelligence
• What is Artificial Intelligence?
• How does AI work?
• What is Generative AI?
• How does an Large Language Model work?
• Tokenization and Example Tokenization: GPT-4o
• Embeddings: Definition, Function, and Importance
• Predicting the Next Token
• LLM Settings: Temperature, Top-P, and Top-K
• How are LLMs trained?
• How do multi-modal models work?
• Full Generative AI architecture
• Benefits and Challenges of Generative AI
• Common Frameworks and Models
• Common Terms in Generative AI

Basics of Prompt Engineering

• Basics of Prompting and Prompt Engineering
• The Art and Science of Communicating with LLMs
• Addressing Limitations of LLMs through Prompt Engineering
• Effective Prompt Design
• Principles of Developing Using Instructions, Questions, and Personas
• Layering Prompts and Audience Adaptation
• Concepts of Summarization, Classification, and Named Entity Recognition
• Case Study: Customer Support Chatbot
• Effective Prompting Strategies and Key Takeaways
• Resources for Mastering Prompt Engineering

Real-world Use Cases and Ethics of AI/ML

• Applications in Various Sectors: Personalized Learning, Fraud Detection, Medical Diagnosis, and more
• AI in Government Administration, Energy Management, and Human Resources
• Understanding AI Ethics and Ethical Progression
• NIST AI Risk Management Framework and Characteristics of Trust
• NIST and EU AI Risk Categories and Examples