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.
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