The AI Search Handbook
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Search stopped handing people a list. It started writing them an answer.
The AI Search Handbook is the complete, self-contained guide to being the source an AI assistant names. It explains the machinery underneath — how retrieval, chunking and citation actually work — then turns it into practical craft you can apply without a technical background.
Why readers buy it
Written for the marketer who has to make this work on Monday.
Most writing about AI search is either panic or a list of tactics with a six-month half-life. This is neither.
The AI Search Handbook is a complete training path from traditional marketing into Generative Engine Optimisation. It does not assume a technical background and it does not require any other source: every concept it depends on — how language models work, how retrieval and chunking operate, why a crawler cannot read your prices — is explained inside the book. It is deliberately unexcited. Where the evidence for a practice is strong, it says so. Where a widely repeated tactic has been contradicted by data, it says that too, names the study, and shows you how to judge the next claim yourself.
- 46 chapters, from how language models work to a 90-day plan
- 41 original diagrams — none of the mechanics left abstract
- 160 self-check questions, every one with a worked answer
- A continuous case study, quick-reference card, glossary and templates
The curriculum
Ten parts, from the machinery to the working week.
Each part builds on the one before it. Parts I and II explain how AI search actually works; everything after that is craft.
- I · Foundations — what actually changed, how search engines work, how language models work, and the six-stage pipeline from question to citation.
- II · The Landscape — Google AI Overviews and AI Mode, the five assistants and how they differ, and a method for judging any claim you read.
- III · Technical Foundations — AI crawlers and the blocks you did not know about, rendering, architecture, structured data, and an honest verdict on llms.txt.
- IV · Content — writing passages a machine can lift, entities, E-E-A-T, citable assets, decay, international and multimodal.
- V · Authority — links versus mentions, share of model, the platforms that feed the models, digital PR, and fixing wrong answers about you.
- VI · Commerce and Place — e-commerce and the product graph, what to do with the visitor a citation earns you, and local GEO.
- VII · Measurement and Proof — what can honestly be measured, building the system, attribution, and experiment design with a control group.
- VIII · Practice — the audit playbook, the toolkit, using AI to do the work, a prompt library, the 90-day plan and the practitioner's week.
- IX · Horizons — agentic search, and the ethics and second-order effects, including a fair statement of the case for all of this.
- X · Reference — glossary, every checklist in one place, templates, 160 worked answers, sources with locators, and a generated index.
Inside the PDF
Full pages from The AI Search Handbook.
Scroll sideways through real pages: the quick-reference card, the retrieval pipeline, the writing craft, the engine comparison and the practitioner's cadence.
How it teaches
Built to be learned from, not skimmed.
Every chapter opens with objectives and closes with a summary and self-check questions. All 160 questions have worked answers at the back, so you find out whether you understood it or only think you did. Forty-one original diagrams carry the mechanics for visual learners.
A single company runs through all 46 chapters as a continuous worked example: a Leeds coffee-equipment retailer whose editorial traffic fell 34% when AI Overviews started answering the questions its guides were built to win. You see what they did, in order, including the fixes that failed and the sprint they wasted. It is a composite rather than a client, and the book says so plainly on its second page.
- Objectives, summary and self-check in every chapter
- 160 worked answers — compare rather than copy
- Reading paths for an afternoon, a week, or a specific problem
- Quick-reference card, glossary, checklists, templates and an index
Questions
Questions people ask before buying.
Do I need a technical background?
No. It is written for marketers with no coding background, and every concept it depends on is explained inside it — including how language models work and why a crawler may not be able to read your prices.
Is this just SEO with a new name?
Substantially the foundations are the same, and the book says so rather than pretending otherwise. What is new is that retrieval works at passage level rather than page level, which changes how you structure content, and that measurement works by sampling rather than rank tracking.
Will it go out of date?
Partly, and it tells you which parts. Retrieval mechanics and entities change slowly. Products and percentages date fast, so every time-sensitive figure carries its date and source. Corrections are published at the repository linked inside the book.
What format is it?
A single 274-page PDF, typeset for screen and print. After Stripe checkout you receive a private signed download link in the browser and by email.
Who wrote it?
Luke McLaughlin, a digital marketing manager in Munich with ten years running organic growth for technical B2B products, most recently across eight international markets.
Book purchase
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