1
Front Matter
5 chapters2
Part 1: Foundations — Understanding What You're Working With
7 chapters- Part 1: Foundations — Understanding What You're Working With
- Chapter 1: What AI Tools Actually Are (and Aren't)
- Chapter 2: How Language Models Think: A Conceptual Framework
- Chapter 3: The Right Mental Models for AI Collaboration
- Chapter 4: Trust Calibration — What AI Gets Right, What It Gets Wrong
- Chapter 5: Setting Up Your Personal AI Environment
- Chapter 6: The Iteration Mindset — Working in Loops, Not Lines
3
Part 2: The Art of Prompting
8 chapters- Part 2: The Art of Prompting
- Chapter 7: Prompting Fundamentals: Structure, Clarity, and Specificity
- Chapter 8: Context Is Everything: Loading Your AI's Working Memory
- Chapter 9: Instructional Prompting and Role Assignment
- Chapter 10: Advanced Prompting Techniques
- Chapter 11: Prompt Engineering Patterns for Recurring Tasks
- Chapter 12: Multimodal Prompting: Working with Images, Documents, and Data
- Chapter 13: Diagnosing and Fixing Bad Outputs
4
Part 3: Working with the Major AI Platforms
7 chapters- Part 3: Working with the Major AI Platforms
- Chapter 14: Mastering ChatGPT and GPT-4
- Chapter 15: Working with Claude: Strengths, Quirks, and Best Practices
- Chapter 16: Google Gemini and the Workspace Integration
- Chapter 17: GitHub Copilot and AI Code Assistants
- Chapter 18: Image Generation — Midjourney, DALL·E, and Stable Diffusion
- Chapter 19: Specialized and Domain-Specific AI Tools
5
Part 4: AI Across Professional Workflows
10 chapters- Part 4: AI Across Professional Workflows
- Chapter 20: Writing and Editing with AI
- Chapter 21: Research, Synthesis, and Information Gathering
- Chapter 22: Data Analysis and Visualization
- Chapter 23: Software Development and Debugging
- Chapter 24: Project Planning and Task Management
- Chapter 25: Decision Support, Analysis, and Strategic Thinking
- Chapter 26: Presentations, Slides, and Visual Communication
- Chapter 27: Business Communication: Email, Reports, and Documents
- Chapter 28: Customer-Facing Work: Sales, Support, and Outreach
6
Part 5: Critical Thinking, Verification, and Ethics
7 chapters- Part 5: Critical Thinking, Verification, and Ethics
- Chapter 29: Hallucinations, Errors, and How to Catch Them
- Chapter 30: Verifying AI Output — Fact-Checking Workflows
- Chapter 31: Understanding AI Bias and How It Surfaces
- Chapter 32: When NOT to Use AI (and Why That Matters)
- Chapter 33: Ethics of AI Use — Disclosure, Attribution, and Fairness
- Chapter 34: Legal and Intellectual Property Considerations
7
Part 6: Advanced Techniques and Automation
6 chapters- Part 6: Advanced Techniques and Automation
- Chapter 35: Chaining AI Interactions and Multi-Step Workflows
- Chapter 36: Programmatic AI — APIs, Python, and Automations
- Chapter 37: Custom GPTs, Assistants, and Configured AI Systems
- Chapter 38: Deploying AI in Teams and Organizations
- Chapter 39: Measuring Effectiveness: ROI, Quality, and Iteration Cycles
8
Part 7: Staying Current and Looking Forward
4 chapters9
Appendices
9 chapters- Appendix A: Prompt Templates Library (Quick-Reference)
- Appendix B: Python Code Reference for AI APIs
- Appendix C: Workflow Templates & Worksheets
- Appendix D: Tool Comparison Quick-Reference Cards
- Appendix E: FAQ — Common Questions and Frustrations
- Appendix F: Key Studies Summary (AI Cognition, Productivity Research)
- Appendix G: Glossary
- Appendix H: Answers to Selected Exercises
- Appendix I: Bibliography
Explore Related Books
More open-access textbooks from our library
Advanced COBOL 305 pages AI Ethics 304 pages AI Literacy 40 pages AI & ML for Business 304 pages AI Engineering 307 pages Algorithmic Addiction 303 pages Applied Psychology 303 pages College Football Analytics 213 pages Creator Economy 318 pages Pattern Recognition 322 pages Data & Society 305 pages Ethical Hacking 318 pages Fandom 332 pages History of Appalachia 324 pages How Humans Get Stuck 285 pages Handling Confrontation 306 pages How Your House Works 306 pages IBM DB2 282 pages Intermediate COBOL 334 pages Intro CS Python 44 pages Intro to Data Science 266 pages Introductory Statistics 216 pages Learning COBOL 322 pages Prediction Markets 316 pages Metacognition 222 pages Media Literacy 314 pages NFL Analytics 182 pages Physics of Music 316 pages Political Analytics 324 pages Basketball Analytics 214 pages Soccer Analytics 230 pages Propaganda 304 pages Python for Business 298 pages Quantum Mechanics 303 pages RegTech 307 pages Science of Seduction 320 pages Sports Betting 322 pages Architecture of Surveillance 299 pages Science of Luck 306 pages Vibe Coding 316 pages Why They Watch 308 pages