AI search optimization is the work of improving how a business appears in AI answers. That includes accurate descriptions, relevant recommendations, and citations to useful pages. This guide covers the technical work, content, research, measurement, and planning involved.
Start with the explanations if you are new to search marketing. If you already do SEO, use the chapters on model memory, retrieval, and measurement to understand where the methods differ. Practical chapters explain the task and link to the relevant technical background.
Three ways to read it
Read the introduction and selected chapters. The navigation marks a shorter reading sequence as “afternoon.” These chapters cover AI search, terminology, retrieval, evidence, measurement, strategy, and a 90-day plan. Use the remaining chapters for topics that need more detail.
Start from your SEO experience. Read SEO and AI search optimization, Model Memory and Live Retrieval, and the visibility research. Then review the evidence on backlinks.
Use a chapter during a project. Start with the problem you need to solve: access, content, inaccurate business information, measurement, or planning. Each chapter explains the terms needed to follow its advice.
The terms this book commits to
This guide uses AI search for the activity and products, AI search visibility for the observed outcome, and AI search optimization for the work. SEO and paid placement are included in that broad scope. The terminology chapter explains how GEO, AEO, and LLMO overlap with those terms.
How this guide uses evidence
Provider documentation, research papers, and recorded experiments support different types of claims. The research chapter identifies the available OppAlerts data and its limits. Examples invented to explain a method are labeled as examples.
For a complete practical sequence, read the worked audit, then use the workbook and checklist. The separate How AI Search Works guide contains the recorded conversation and hotel prompt experiment.
Source dates and revisions
This edition was revised on September 10, 2026. Product features and provider controls can change. Dated research results describe their collection conditions; an editorial revision does not make an old experiment current. The revision record lists the changes made in this edition.