Chapters of LLM Mastery: How AI Recommends Brands for LLM Domination
The book has the Introduction and 14 chapters, between a short note from the seven authors and a closing section called Take Action.
LLM domination means one thing: when the machines are asked about your market, they say your name.
How many chapters does the book have?
LLM Mastery: How AI Recommends Brands for LLM Domination has the Introduction and 14 chapters.
The list below is the whole book in printed order, so it has 17 entries: the opening note, the Introduction, the 14 chapters and the closing section, Take Action, each with its own first sentence. The chapter numbers are the book's own; the note, the Introduction and Take Action carry none.
- A Note Before We Begin“Seven of the market leaders in AEO and GEO wrote this book, and one of us is a digital avatar.”
- Introduction: Domination Is Being the Answer“LLM domination means one thing: when the machines are asked about your market, they say your name.”
- 1How LLMs Actually Answer“Every LLM answer is assembled from two layers, memory and retrieval, fused by a synthesis step, and brands live or die in all three.”
- 2How AI Recommends Brands“A brand recommendation is the output of a pipeline, and the pipeline has four stages: recognise, evidence, confidence, name.”
- 3Every LLM Is Different (Test Them Like It)“The models disagree with each other constantly, and treating them as one machine is the most common strategic error in AI search.”
- 4AI Consensus: Make Every Model Agree on You“AI consensus is the state where every model, on every surface, reaches the same conclusion about your brand, and it is the real asset this entire discipline builds.”
- 5The Test Test Test Method“Everything in AI search is testable, and the operators who test own the operators who theorise.”
- 6Campaign Research: The Five Datasets That Decide GEO and AEO Strategy“Every LLM answer you log contains five datasets, and together they are the complete research layer of a GEO and AEO campaign.”
- 7Seeding the Training Layer“The training layer is won by being consistently, repeatedly, factually present across the web the models learn from, before they learn it.”
- 8Winning the Retrieval Layer“The retrieval layer is won by holding the positions models fetch mid-answer, and traditional search competence still decides most of it.”
- 9Listicle Frameworks for AI Crawlers“Listicles are the format machines lift rankings from, and engineering them properly is one of the highest-leverage skills in AI search.”
- 10Entity Foundations for LLM Visibility“Every tactic in this book leaks without entity foundations, because evidence only counts when the machines can confidently attach it to you.”
- 11Off-Page Signals LLMs Trust“LLM trust is built off your own site, on surfaces you do not control, and a decade of off-page discipline translates directly once you know what changed.”
- 12Owning the Five Money Queries“Brand traction spawns five query shapes, and dominating a market means covering all five, because buyers are won at discovery and lost at the decision.”
- 13Measuring LLM Domination“Domination is measured as share of the answers that matter, tracked monthly, per model, against the log.”
- 14The Domination Playbook“LLM domination is built in a sequence, and the sequence exists because each stage stops the next one leaking.”
- Take Action“Here is the uncomfortable truth to close on. Most of the people who read this book will never run a single test.”
How is the book organised?
The Introduction sets out the order itself: how models assemble answers, the testing discipline and its five research datasets, the build, and the measurement that tracks it.
After the Introduction, the chapters fall into five runs, and the last, Chapter 14, turns the rest into a sequence of steps.
- How the models answer, choose brands, differ and agree (Chapters 1 to 4): How LLMs Actually Answer, How AI Recommends Brands, Every LLM Is Different (Test Them Like It) and AI Consensus: Make Every Model Agree on You
- The testing discipline and its five research datasets (Chapters 5 and 6): The Test Test Test Method and Campaign Research: The Five Datasets That Decide GEO and AEO Strategy
- The build (Chapters 7 to 12): Seeding the Training Layer, Winning the Retrieval Layer, Listicle Frameworks for AI Crawlers, Entity Foundations for LLM Visibility, Off-Page Signals LLMs Trust and Owning the Five Money Queries
- The measurement (Chapter 13): Measuring LLM Domination
- The playbook that puts it in order (Chapter 14): The Domination Playbook
How does the book close?
The book closes with Take Action, which opens: “Here is the uncomfortable truth to close on. Most of the people who read this book will never run a single test.”
The closing section says most readers will never run a single test, warns that procrastination dresses itself up as being sensible, and asks the reader to build the prompt bank that night and run the baseline that week.
Its last line is the last line of the book.
Where can I read a chapter in full?
The note and the whole Introduction are free to read on this site, with no email address asked for.
Together they run to 791 words. Chapters 1 to 14 and Take Action are in the ebook; each chapter's page here gives its first and last lines and its named parts.