Introduction to Generative AI Concepts 

Duration: 1 Day / 8 hrs

Introduction 

This one-day course provides a clear and practical introduction to Generative AI concepts, covering how modern AI systems work, how large language models generate content, and how to interact with them effectively using prompt engineering. The focus is on building strong fundamentals with real-world examples and hands-on practice. 

Objectives 

By the end of this Generative AI fundamentals training, participants will be able to: 

  • Understand core AI and Generative AI concepts, including LLMs and tokenization 
  • Explain how Generative AI models generate text and multi-modal outputs 
  • Apply prompt engineering techniques for common business and technical use cases 
  • Identify ethical risks and apply responsible AI principles 

Key Takeaways 

Participants will leave with: 

  • A solid foundation in Generative AI and Large Language Models 
  • Practical skills to write effective prompts for real scenarios 
  • Awareness of ethical, regulatory, and risk considerations in GenAI usage 
  • Confidence to explore advanced GenAI tools and applications 

Training Methodology (Learning by Doing) 

  • Hands-on labs throughout the day—learn by experimenting, not just listening 
  • Real-world use cases like content generation, summarization, and chatbots 
  • Interactive demos to visualize how LLMs respond to prompt changes 
  • Iterate and refine prompts to understand model behavior deeply 

Understanding Generative AI 

Key Concepts 

  • What is Intelligence? Exploration of cognitive mechanisms and artificial replication. 
  • Mechanisms of Intelligence Biological vs. computational models. 
  • What is Artificial Intelligence? Definitions, types, and evolution. 
  • How Does AI Work? Algorithms, data, and feedback loops. 

Generative AI Foundations 

  • Definition and Scope GenAI as a subset of AI focused on content generation. 
  • Large Language Models (LLMs) 
  • How they work 
  • Tokenization (with GPT-4o examples) 
  • Embeddings: role and importance 
  • Predicting the next token 
  • LLM settings: Temperature, Top-P, Top-K 
  • Training processes 
  • Multi-modal model capabilities 
  • Full GenAI architecture overview 

Prompt Engineering 

Basics 

  • Prompting vs. Prompt Engineering Crafting effective inputs for LLMs. 
  • Communicating with LLMs Clarity, structure, and intent. 

Advanced Techniques 

  • Designing Effective Prompts 
  • Instructions, questions, personas 
  • Layering prompts 
  • Audience adaptation 
  • Use Cases 
  • Summarization 
  • Classification 
  • Named Entity Recognition 
  • Case Study: Customer Support Chatbot 
  • Strategies & Takeaways 
  • Addressing LLM limitations 
  • Iterative refinement 
  • Resource recommendations 

Ethics and Risk Management 

Responsible AI Use 

  • Ethical Progression in AI From awareness to accountability. 
  • NIST AI Risk Management Framework Characteristics of trust, transparency, and fairness. 
  • Risk Categories 
  • NIST and EU classifications 
  • Real-world examples 

Hands-On Labs 

Real-World Scenarios 

  • Marketing Copy Generation 
  • Document Summarization 
  • Data Analysis with Prompts 
  • Chatbot Simulation