PDF Summary:The ChatGPT Millionaire, by Neil Dagger

In The ChatGPT Millionaire, Neil Dagger explores how to leverage ChatGPT and similar AI tools to generate income by creating content, building passive revenue streams, and enhancing your freelance work. Dagger presents practical strategies for using AI to develop ebooks, online courses, affiliate marketing content, and more—with an emphasis on maximizing productivity while maintaining quality.

Our guide distills Dagger’s techniques while providing additional context on how the AI landscape has evolved since publication, including considerations about market saturation and emerging best practices. We complement Dagger’s technical approaches with discussions of ethical considerations, sustainable value creation, and the role of human expertise in an AI workflow. Whether you’re a content creator, an entrepreneur, or a professional wanting to work more efficiently, this guide helps you navigate both the opportunities and limitations of integrating AI tools into your work.

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(Shortform note: Users may misunderstand how well ChatGPT is equipped to handle knowledge in domains where the user might be a novice and place too much confidence in the model. If you’re working in a subject area where you don’t have much expertise, the model may produce convincing-sounding content that contains subtle but significant errors or outdated information that you don’t immediately recognize as inaccurate. Plus, contrary to popular belief, ChatGPT doesn’t “learn” from your conversations. Each new chat starts fresh, and your interactions don’t update its knowledge base.)

How Can You Use ChatGPT Effectively?

Dagger offers a basic framework for maximizing the value you get from ChatGPT, centered on using strategic prompting techniques. His core principle is straightforward: The quality of output directly correlates with the quality of input you provide. He outlines several key approaches to improve your results when using ChatGPT, which we’ll take a closer look at in this section. (Shortform note: The prompting strategies remain generally applicable to current versions of ChatGPT, even though the technology has evolved since the book’s publication.)

Beyond “Garbage In, Garbage Out”

Dagger’s idea that there’s a direct correlation between the quality of an LLM’s input and its output might oversimplify how LLMs actually work. While this principle resembles the computing concept called “garbage in, garbage out,” LLM prompting isn’t a direct input-output relationship, and the correlation isn’t always linear or predictable. Unlike traditional programming (where precise inputs yield predictable outputs), LLMs operate through complex pattern recognition across massive datasets. Sometimes, a detailed prompt produces mediocre results because it doesn’t align well with the model’s training patterns, while a simpler prompt might hit a sweet spot and generate exceptional results.

AI expert Lincoln Murphy emphasizes that running prompts without adequate context is usually what leads to disappointing results. He contends that the key isn’t crafting clever prompts, but providing the right contextual framework to help the model understand your needs and goals. You might think of prompting as more like teaching than programming: You’re guiding the model toward your goal through a collaborative process, not executing a fixed command. This means the real skill lies in understanding how the model processes data and learning to communicate in ways that activate its most relevant knowledge patterns.

Prioritize Clarity in Your Prompts

The first strategy that Dagger recommends is writing clear, unambiguous instructions that explain the task in terms that AI can understand. Before engaging with ChatGPT, he recommends having a well-defined goal for what you want to accomplish. Being specific about your requirements—including the tone, format, and length of the response you want, as well as the purpose of the desired response—helps the model generate more relevant content. This advice remains practical regardless of which version of ChatGPT or similar AI tool you might use.

Tell GPT What Role to Assume

A second technique Dagger highlights is the “act as” method. By instructing ChatGPT to “act as” a particular professional—such as a marketing expert, novelist, nutritionist, or programmer—you can elicit responses that better align with industry standards. This approach effectively taps into the model’s training on content that was originally created by experts in various fields, resulting in more specialized and appropriate outputs.

The Ethical Implications of Relying on “AI Expertise”

When Dagger recommends instructing ChatGPT to “act as” an expert in various fields, he highlights a valuable technique for generating specialized content—but he doesn’t address ethical considerations about representation and authenticity that have emerged since the book’s publication. One significant concern is the potential for misleading others when AI-generated content falsely presents expertise it doesn’t possess. Experts suggest that disclosure is essential when you use AI in professional contexts. This is particularly important in fields like health care, finance, or law, where inaccurate information can have serious consequences.

Another challenge is the difficulty of verifying the model’s information: Since ChatGPT can’t reliably fact-check its own statements—though competitors like Perplexity and Copilot can link to their sources—users may struggle to identify errors in the model’s writing on unfamiliar subjects. As one researcher notes, “ChatGPT has one crucial flaw, which is that it doesn’t know when it doesn’t know something.” While the “act as” technique can improve output quality, responsible use may require transparency about AI involvement and verification from genuine subject matter experts, especially in consequential domains.

Ask for Multiple Options

A third strategy Dagger suggests is that rather than settling for a single output, you can request several alternatives simultaneously. For example, instead of asking ChatGPT to write a single headline for a blog post, an email subject line, or a product description for your Etsy store, you can prompt it to create several alternatives and try out different angles on the same idea. This gives you the ability to exercise your judgment in selecting the most effective option or combining elements from different versions. This technique acknowledges that the first AI-generated response isn’t always the best one—and that LLMs “think” by writing.

Always Save Time to Review and Edit

Fourth, Dagger cautions against using ChatGPT’s output without closely reviewing its writing yourself. He suggests that you should make a practice of treating AI-generated content as a first draft that needs checking for accuracy, tone, and style, rather than considering it a finished output.

(Shortform note: Dagger’s recommendation to review AI-generated content aligns with best practices advocated by AI ethics experts, though they recommend this practice for different reasons. While Dagger primarily emphasizes quality control, experts highlight broader concerns. Some recommend keeping a “human in the loop” to ensure AI systems remain aligned with your values and intentions. If you don’t, then inaccurate AI-generated content can spread online and potentially contribute to misinformation.)

Break Tasks Into Smaller Pieces (and Prompts)

For complex projects, Dagger advises breaking tasks into manageable parts rather than attempting to accomplish everything with a single prompt. This incremental strategy enables you to take a hands-on approach to guiding the development of longer content and to provide the model with feedback on its work at each stage. For example, if you’re writing a book, you might start with prompts about the overall concept, then move to chapter outlines, and finally to drafting individual sections—reviewing, refining, and providing ChatGPT with valuable guidance at each step of the process.

Diving Deeper: Charting a Course for Better Prompts

Dagger’s framework offers starting points for effective prompting, but thinking about how you’d implement these strategies on a real project reveals some nuances that are also worth exploring. To illustrate how these techniques work together (and how you might want to supplement Dagger’s suggestions with additional strategies), let’s consider a practical example: A marine biologist preparing content for her museum’s new blue whale exhibit needs to create an engaging blog post about whale migration.

Clarity and context serve as the foundation of effective prompting. Rather than simply asking for “information about whale migration,” our biologist would specify: “Write a 750-word blog post about blue whale migration patterns across the Pacific Ocean, using an educational tone appropriate for natural history museum visitors ages 12 and up. Include information about how blue whales typically migrate between summer feeding grounds in polar waters and winter breeding grounds in more equatorial regions.” This specificity transforms a generic request into a focused task with clear parameters. The results would be even better (and likely more accurate) if the biologist provides the model with context like scientific papers.

The “act as” technique—akin to providing a model with a system prompt—can significantly enhance an LLM’s ability to produce specialized content. Our biologist might prompt ChatGPT to “Act as a marine biologist who specializes in explaining cetacean migration to public audiences.” While Dagger highlights this approach’s benefits, researchers say that for technical subjects, it helps to add specificity to your prompt. You might tell ChatGPT to “focus particularly on explaining how blue whales’ migration routes are determined by food availability, especially the seasonal concentrations of krill in different ocean regions,” or even that it should act as a biologist who specializes in studying these migration routes.

Requesting multiple options from the model will give you access to creative alternatives beyond the obvious first answer that the model might generate as a response to your initial prompt. Instead of asking for a single introduction for her blog post, our biologist could ask ChatGPT to “Generate three different approaches to opening this article: one using a surprising statistic about blue whales’ feeding capacity (up to 6 tons of krill daily), one describing their distinctive mottled blue-gray coloration, and one explaining how scientists use satellite tags to track these massive creatures.” This variety offers conceptual paths that a human expert might not have initially considered for engaging museum visitors.

Breaking complex tasks into steps—such as by using what experts call “chain-of-thought prompting” to guide an LLM through the reasoning it should follow—allows for more accurate work. Rather than requesting the entire blog post at once, a more effective approach for our biologist might be to structure the task as a series of steps:

• “Outline the major sections for an article on blue whale migration, including feeding behavior in polar waters, the timing of seasonal movements, and the role of ocean acoustics in their navigation.”

• After reviewing ChatGPT’s outline, the biologist might request: “Develop the section on how blue whales communicate during migration, focusing on their vocalizations that can travel up to 1,000 miles through ocean waters.” She would continue on to ask ChatGPT to develop the other sections of the post, one section at a time.

• After ChatGPT has written a draft of each section, she might ask: “Now integrate these sections with appropriate transitions, emphasizing how their migration routes are driven primarily by krill availability rather than fixed geographic pathways.”

This incremental approach facilitates continuous review and refinement—a step Dagger mentions primarily for quality control but that other experts say serves multiple purposes. Beyond editing the LLM’s output for grammar and style, this process allows the biologist to identify potential inaccuracies in the model’s writing, or to ask it to be more specific in the details. “In the section on swimming speeds, replace the general statement about ‘fast swimming’ with specific information about how blue whales typically travel at 5 miles per hour while feeding but are capable of accelerating to 20 miles per hour for short bursts.”

While Dagger doesn’t address verification strategies, our biologist would also need a process to ensure the scientific accuracy of her blog post. Unlike ChatGPT (which cannot reliably cite its sources), several AI tools like Perplexity and Bing include source citations with their responses, which enables users to verify information directly. If she doesn’t have access to one of these AI tools, the biologist could verify key facts against trusted scientific sources before publishing her post. Alternatively, she could directly provide ChatGPT with source material: “Using only information from this paragraph from the NOAA website on blue whale migration [insert text], explain how blue whales’ movements are driven by krill availability.”

How Can You Make Money With ChatGPT?

Finally, Dagger outlines strategies for leveraging ChatGPT to generate income based on how he thinks AI tools and usage will evolve. He presents two primary approaches: creating passive income streams and enhancing freelance work.

Generate Passive Income

Dagger cautions that simple, quickly-completed ChatGPT tasks will rapidly oversaturate markets as more people discover these opportunities. (Shortform note: This prediction has largely proven accurate: The marketplace is flooded with basic AI-generated content.) He suggests that AI’s greatest potential lies in more complex projects because higher barriers to entry create more sustainable opportunities. While anyone can use ChatGPT to generate a simple product description or social media post, creating a comprehensive ebook, detailed course, or functional app requires deeper subject knowledge, technical skills, and sustained effort. The passive income opportunities Dagger identifies include:

Writing Ebooks

Dagger suggests ChatGPT can help research topics, outline chapters, draft content, and create marketing copy to sell ebooks on platforms like Amazon. At the same time, he acknowledges that developing a truly valuable book requires careful curation and multiple iterations.

(Shortform note: Since Dagger published this book, the ease of generating ebook content with AI has led to a flood of low-quality publications, making it harder than ever for genuinely valuable books to find readers. But you can still use AI to help you create the best version of the book you want to write: Experts recommend using LLMs to help you organize and articulate your knowledge in the areas where you’re an expert. For example, you might use AI to suggest how to structure your book in a way that engages readers while focusing on contributing your expertise, knowledge of relevant research, and original insights.)

Producing YouTube Content

Dagger describes how ChatGPT can help identify potential video topics with high search volume but low competition, then generate scripts you can use to create videos. If you have an established presence on YouTube, you can monetize these videos through advertising, sponsorships, or product promotions.

(Shortform note: Dagger points out that video production involves multiple steps beyond just generating a script, and research shows YouTubers use AI tools strategically at different stages of production, like brainstorming video topics, creating video descriptions optimized for search, and generating b-roll ideas or talking points. Content creators say they rarely use AI for the entire production process, and as one production expert notes, relying too much on ChatGPT can get in the way of developing your skills and voice. The key might be to use AI to handle routine aspects of production while reserving your creative energy for the elements of each video that make your content unique.)

Creating Affiliate Marketing Blog Posts

According to Dagger, ChatGPT can help you craft product comparison articles or buying guides that incorporate affiliate links. He suggests focusing on niches where you have expertise, which would enable you to evaluate and supplement ChatGPT’s output. For example, a fitness enthusiast might use ChatGPT to help draft reviews of exercise equipment they’ve personally tested, adding their own insights and experiences to make the content more authentic and valuable.

(Shortform note: Other experts agree with Dagger that instead of creating affiliate marketing posts for whatever category of products pays the highest commission, you’ll see better results by focusing on an area you’re passionate about and writing about products you’ve actually used. You might use ChatGPT as a “sounding board” to refine your ideas for new posts, or you could use it to help you analyze data—from traffic metrics to reader demographics—to support you in making informed decisions about what kinds of content you want to focus on.)

Building Online Courses

Dagger writes that online courses potentially offer the highest return on investment. ChatGPT can help structure courses, develop lecture scripts, create supplementary materials, and design quizzes. He mentions combining ChatGPT with other AI tools (like Pictory, which can help you convert scripts into videos) to streamline production.

(Shortform note: Experts offer tips on how to ensure your AI-generated course content is accurate, engaging, and educational. College professors who’ve integrated AI into their teaching recommend treating the technology as a collaborator rather than a replacement and using your experience to provide context, real-world examples, and feedback that AI cannot. Start by defining your learning objectives before generating any content: Consider what skills students should master and how you’ll measure their understanding. Then structure your course to encourage critical thinking rather than passive consumption, adding opportunities for students to analyze, apply, and evaluate information rather than merely absorb it.)

Writing Code and Apps

For those with a technical background, Dagger suggests ChatGPT can generate functional code, create documentation, test for bugs, and help troubleshoot issues. This application remains relevant, though ChatGPT is better suited for helping with specific coding problems than generating complete, production-ready applications.

(Shortform note: Current LLMs are still limited in their capacity to produce complete, production-ready code, and even advanced models struggle with logical errors, missing code blocks, and incorrect implementation of complex features. But coding capability appears to be a key focus for AI companies going forward. Anthropic CEO Dario Amodei predicts that “AI will write 90% of the code for software engineers within the next three to six months,” and Mark Zuckerberg has said that Meta is developing AI that can function as a midlevel engineer. Developers are also creating innovative approaches to AI-assisted coding like “test-driven” code generation, which uses “unit tests” to guide LLMs toward correct implementations.)

The Passive Income Paradox

The pursuit of passive income isn’t new. From the content farms of the early 2010s to dropshipping and cryptocurrency, every few years presents a new gold rush promising maximum return for minimum effort. The AI-generated content boom follows this same pattern. According to marketing expert Neil Patel, the fundamental issue with many passive income strategies is that they emphasize “revenue extraction” rather than “value creation.” Revenue extraction focuses primarily on maximizing profit rather than providing benefit to customers. In contrast, value creation prioritizes delivering something useful, unique, or beneficial, with revenue as a natural byproduct.

Dagger acknowledges that creating truly valuable content requires careful curation and multiple iterations, and since the publication of his book, experts have increasingly emphasized the importance of prioritizing output quality over quantity. As the markets for AI-generated content become increasingly saturated (which Dagger himself warns about), experts recommend focusing on creating content that provides genuine value. This shift in thinking—from using AI primarily to save time to using it to enhance your existing expertise—may help overcome the saturation issues Dagger identifies by helping you build a lasting audience and tailor your content to their needs.

Enhance Your Freelance Work

In addition to generating passive income, Dagger suggests that freelancers can use ChatGPT to increase productivity by automating routine aspects of their work. Whether for translation, copywriting, or content creation, ChatGPT might help you produce initial drafts more efficiently. Dagger maintains that combining ChatGPT’s efficiency with human expertise creates more sustainable monetization strategies.

(Shortform note: Because of the rapid adoption of LLMs—with ChatGPT reaching approximately 800 million users—what Dagger describes as an opportunity to gain competitive advantage has quickly become a baseline expectation in many freelance fields. Research shows that professional writers increasingly use LLMs not to replace their work but to streamline specific aspects of their process, from research assistance to structural feedback. Rather than viewing AI tools as optional productivity boosters, many freelancers now consider them essential collaborative partners that allow them to maintain competitive rates while meeting client expectations for both quality and turnaround time.)

From Theory to Practice: How The ChatGPT Millionaire Models Its Own Advice

The ChatGPT Millionaire represents a case study in AI-assisted publishing success. According to self-publishing analyst Kristen Walters, the book sold more than 6,000 paperback copies in just 30 days, generating approximately $85,000 in revenue—and demonstrating the potential market for practical AI guidance. Walters notes that Dagger acknowledged using Jasper AI to write the book and included affiliate links to the platform. This approach mirrors a common strategy in digital marketing: creating content that serves readers directly while generating additional revenue through affiliate relationships. For freelancers implementing Dagger’s advice, industry experts highlight several best practices:

Transparency as a professional advantage: The American Society of Journalists and Authors and other professional organizations recommend disclosing significant AI usage to clients. Interestingly, studies show this transparency can enhance rather than diminish client trust. When freelancers articulate how AI augments their expertise (saving time on research or formatting while preserving their unique insights), clients typically appreciate both the efficiency and honesty.

Market positioning considerations: Experts note that the marketplace for AI-generated content has evolved significantly since Dagger’s book was published, with consumers becoming more sophisticated about distinguishing generic AI-generated content from work that combines the efficiency of AI tools with valuable human insight. Freelancers who position themselves as skilled AI collaborators—able to guide, refine, and enhance AI outputs with their specialized knowledge—can often offer more value (and command higher rates) than those simply offering high-volume, purely AI-generated content.

Quality assurance practices: Many clients now use content verification systems, including AI detection tools. Professional content creators typically develop workflows that include substantial human editing and refinement of AI-generated drafts, which is one way to ensure that the content you create reflects the quality standards your clients expect. Experts also note that editing AI-generated content has become a valuable (and marketable) skill in itself.