Prerequisites and Assumed Knowledge

What You Need to Begin

Almost nothing technical. This book is designed for anyone who wants to use AI tools effectively, regardless of technical background. Here is what we assume:

What We Assume You Have

1. Access to at Least One AI Tool

You should have access to at least one of the major AI chat interfaces: - ChatGPT (free or paid) — at chat.openai.com - Claude (free or paid) — at claude.ai - Google Gemini — at gemini.google.com - Microsoft Copilot — integrated into Microsoft 365 or at copilot.microsoft.com

A free account on any of these will be sufficient for most exercises in this book. Some advanced features (GPT-4, Claude Opus, extended context) require paid subscriptions; we'll note when those are relevant.

2. Comfort Navigating a Web Browser

You should be able to open a website, sign in to an account, type in a text box, and read a response. That's it.

3. Willingness to Experiment

This is the most important prerequisite. AI tools reward people who try things, observe what happens, and adjust. If you are uncomfortable with ambiguity or with learning by doing, this book will push you toward that comfort. That's a feature, not a bug.

What We Do NOT Assume

No Programming Knowledge Required

The core of this book (Parts 1–5, Part 7) requires no programming knowledge whatsoever. Python code appears in a few places: - Chapter 5 — optional environment setup with pip install commands - Chapter 36 — programmatic AI access via APIs (clearly marked as technical) - Chapter 22 — optional data analysis examples - Appendix B — Python code reference

If you're a non-programmer, you can skip or skim the code sections without losing the thread of any chapter. Each technical section is self-contained and labeled.

If you are a programmer, the code sections will be directly useful. They assume Python 3.10+ and familiarity with concepts like functions, loops, and API calls.

No Prior AI/ML Knowledge Required

You don't need to know what a transformer is, what RLHF stands for, or how training data works. Chapter 2 gives you the conceptual framework you need — and it goes no deeper than necessary.

No Domain Expertise Required

Whether you work in marketing, law, medicine, engineering, education, or anything else — the principles in this book apply. Where relevant, we give examples across multiple professional contexts.

Technical Setup (Optional)

For readers who want to use the Python code examples in Chapters 36 and Appendix B:

# Install required packages
pip install openai anthropic google-generativeai python-dotenv requests

See requirements.txt in the root of this project for the full list with pinned versions.

You'll also need API keys from: - OpenAI (for GPT-4 API access) - Anthropic (for Claude API access) - Google AI Studio (for Gemini API access)

Chapter 5 walks through the setup process in detail, including how to store API keys securely.

A Self-Assessment: Are You Ready?

Ask yourself these questions. If you can answer "yes" to at least the first three, you're ready to start:

  1. Have I typed something into an AI chatbot and gotten a response? (Even once)
  2. Do I have a real use case I want to improve? (A task, a workflow, a type of output)
  3. Am I willing to be wrong about how AI works and update my understanding?
  4. (Optional) Do I have Python installed on my machine? (Only needed for Ch. 36 and Appendix B)

If you answered "no" to questions 1 or 2, start with Chapter 5, which walks through getting set up from scratch.

If you answered "no" to question 3 — this book will help with that, but you'll need to bring some intellectual flexibility. AI tools consistently confound people who are sure they already understand them.

For Educators Using This Book

If you're using this as a course text, here are suggested paths:

Introductory AI Literacy Course (8 weeks): - Week 1: Chapters 1–2 (What AI is, how it thinks) - Week 2: Chapters 3–4 (Mental models, trust) - Week 3: Chapters 7–8 (Prompting basics) - Week 4: Chapters 9–10 (Advanced prompting) - Week 5: Chapters 20–21 (Writing and research workflows) - Week 6: Chapters 29–30 (Hallucinations and verification) - Week 7: Chapters 33–34 (Ethics and IP) - Week 8: Chapter 42 (Capstone)

Advanced Practitioner Course (12 weeks): - Full Parts 1–3 (Weeks 1–4) - Selected chapters from Part 4 based on cohort (Weeks 5–7) - All of Part 5 (Weeks 8–9) - All of Part 6 (Weeks 10–11) - Part 7 + Capstone project (Week 12)

Workshop Format (1 day): - Morning: Chapters 3, 7, 10 (Mental models + prompting) - Afternoon: Chapters 29, 33 + Appendix D (Verification, ethics, tool cards)