Introduction to Generative AI Concepts

Course Introduction: A friendly, grounded intro to the world of GenAI — LLMs, tokenization, multimodal models, prompt engineering, risks, ethics, and hands-on labs. Ideal for teams who want clarity without the jargon overload.

Target Audience: Beginners, non-technical teams, functional roles, business leaders

Duration: 8 Hrs.

Detailed Table of Contents:

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

Chatbot Simulation

Marketing Copy Generation

Document Summarization

Data Analysis with Prompts