These days headlines scream about robots stealing our jobs and making humans jobless and obsolete. Pam Baker offers a more nuanced perspective: GenAI is not going to take jobs away from most people. Someone good at using GenAI will. Be that someone.

That statement neatly encapsulates the AI dilemma: the fear of losing your job to a machine (the proverbial “stick”) contrasted with the potential to leverage AI to boost your career (the “carrot”). If that’s not enough motivation, consider this: The same professional skills that improve our productivity on the job can also improve our personal and creative lives, offering benefits beyond the workplace.

Pam Baker is a well-qualified guide to this subject. She is an award-winning freelance journalist, analyst, and author. Her new book, Generative AI for Dummies, is a gift to anyone needing a crash course in GenAI—fast. Baker summarizes her target audience in the Introduction: “You are smart and pressed for time and, therefore, want all meat and no fluff in a fast and easy read.” True to its promise, this book fits the bill for busy professionals and curious minds alike.

AI isn’t just evolving quickly. It’s making head-spinning leaps that can leave even seasoned techies breathless. Consider DeepSeek, the latest Chinese entrant in the AI arena. The first time I heard about DeepSeek was from a January 26 Substack post by Ethan Mollick, a Wharton School professor who’s as on top of the AI world as anyone I know.

Less than a week later, DeepSeek had already led to a trillion-dollar loss in the S&P 500. “Disruption” has been an IT buzzword for years, but with AI, disruption takes on a whole new meaning. It’s as if we have jumped from cuneiform tablets to the movie “Oppenheimer” overnight. With transformations happening at warp speed, you’d be forgiven for asking, “Can any single book truly keep up?” Surprisingly, Generative AI for Dummies does as well as we can expect from any paper product.

Like most of Wiley’s “For Dummies” offerings, Baker’s approach is user-friendly, concise, and mercifully free of overly technical jargon. The book is built around clear headings, bullet points, and relatable examples – perfect for readers who’d rather not wade through academic papers or decode dense programming talk.

Chapters 1 through 5 introduce basic AI concepts, focusing on the generative models that power tools like ChatGPT, Gemini, and various text-to-image or text-to-audio applications. The rest of the book builds on these concepts, delving deeper into how AI can be used in the real world – whether you’re a business leader, a freelance writer, or someone trying to figure out how to harness AI for personal projects.

Chapter 2, “Introducing the Art of Prompt Engineering,” deserves special attention. Improving your prompts is the best way to increase the value you receive. Think of prompt engineering like training a bright but literal-minded puppy. The prompt “Write a poem!” will produce something, but being more specific — “Write a short, humorous poem about the joys and perils of online grocery shopping, in the style of Ogden Nash” – leads to more focused, entertaining results.

Baker’s coverage of prompting techniques, including iterative prompting (an ongoing conversation with the AI) and prompt chaining (a structured approach that breaks a request into multiple steps), is incredibly practical. She even shares 15 guidelines to elevate your AI game, from adopting different personas to experimenting with prompt length. If you’ve ever felt like AI just “wasn’t getting it,” these tips can dramatically improve the outputs you receive.

Chapter 6, “Manipulating the GenAI Model to Milk It for More or Better Content,” is another highlight. It explores fundamental weaknesses of AI-like hallucinations (where the AI confidently provides wrong information) – and suggests ways to reduce GenAI mistakes. Recent headlines suggest these techniques might help many lawyers, including the ones who recently filed a brief with eight phony citations and tried to avoid sanctions by drafting an embarrassing confession and apology.

Baker even broaches the idea of “lying” to AI systems to trick them into higher-quality outputs, though she frames this carefully to avoid ethical quagmires. The chapter also examines strategies for guiding AI to desired results, rewarding and punishing GenAI to refine responses, and grappling with the AI’s potential to provide misleading information.

Lying to an AI may sound provocative, but Baker outlines the rationale clearly. For instance, if you’re working on a creative writing project, you might frame your instructions in a way that deliberately tests the AI’s limits. The goal, of course, is not to spread disinformation but to spark the AI into generating novel ideas or better-structured text.

Some of the book’s most advanced tips appear in Chapter 18, “Ten Tips for Advanced Prompting.” These range from systematically refining your prompts (prompt chaining 2.0, if you will) to leveraging new features in the latest AI models. The final tip might be the most transformative: Use “meta-prompts” to ask the AI model how you can ask better questions. For instance: “How can I refine this prompt to get a more comprehensive overview of the impact of blockchain technology on finance?”

It’s a simple yet powerful trick. By guiding the AI to critique and optimize your instructions, you essentially turn the machine into a writing coach. This strategy alone could elevate your interactions from “hit-or-miss” to “well-targeted and productive.”

No book is perfect. If I had one suggestion for a second edition, it would be to beef up the index. Retrieval-augmented generation (RAG)—a central concept mentioned repeatedly—doesn’t appear in the index. This is perplexing, given the many automated indexing tools available these days. Perhaps that’s a job for the next generation of GenAI…or a dedicated human indexer.

So, Can GenAI write a book?

Can GenAI write a book? Yes, but not necessarily a good one. GenAI models struggle with reasoning effectively and cannot produce a professional-grade book on their own.

When used by a talented writer, AI can help produce a great one. This book is proof of that. As Baker explains in the introduction, she used GenAI as a “junior-level assistant” in her drafting process. The result? A readable, insightful, and—dare I say—human take on today’s most exciting and powerful technology.

Bottom Line: Generative AI for Dummies demystifies the complex world of generative AI for audiences from all walks of life. If you’re after a fast, engaging, and practical introduction to AI—and maybe even a little chuckle or two along the way—this book delivers. Baker peppers in real-world stories, specific techniques, and timely advice for using GenAI effectively, whether you’re a startup founder exploring new strategies, a student looking for a research assistant, or a novelist seeking a creative muse.

For anyone unsure of how to make sense of AI’s dazzling potential, Generative AI for Dummies provides a sturdy initial roadmap. It won’t transform you into a machine-learning guru overnight, but it will help you build confidence and curiosity—two essential ingredients for anyone keen on surviving—and thriving—in an AI-driven world.

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Pam Baker, Generative AI for Dummies (John Wiley & Sons, Hoboken, 2025). Available from John Wiley & Sons, Barnes and Noble, and Amazon.