Answer engines like ChatGPT are changing how people search for information. Instead of returning lists of web pages, these systems provide direct answers, sourced from content they can confidently access and interpret. As a result, clicks from traditional SEO efforts continue to decline, making it clear a new approach is needed.
This book introduces the emerging discipline of answer engine optimization, a practical framework for making content more discoverable and citable by generative AI systems. Drawing on decades of experience, author Rodrigo Stockebrand explains how large language models retrieve, evaluate, and decide which sources to include—and not include—in the final answer. You'll explore how to design, structure, and maintain content so answer engines can reliably interpret and reference it, and how to position your organization as a trusted source for AI systems.
You'll also learn how to:
Understand how answer engines evaluate and select information
Structure content to improve comprehension by large language models
Use semantic HTML and structured data to improve content recognition
Build topical authority that supports credibility and citation across platforms
Measure performance across AI systems using emerging tools and metrics
