GEO / AEO: the glossary
and answers to the key questions.

Working definitions for the playbook’s terms, and direct answers to the questions people most often ask about visibility in generative search engines.

Glossary

GEO

Generative Engine Optimization — optimizing content and brand presence for generative search engines; formalized as a task with GEO-bench ^[1].

AEO

A practical market term for improving visibility in AI search experiences; from Google’s standpoint this remains SEO for Search ^[65].

RAG

Retrieval-Augmented Generation: the model first retrieves documents, then generates an answer grounded in them and in its parametric memory ^[24].

Passage / fragment

A self-contained block of content a retrieval system can extract in many scenarios; not a Google requirement to split the page ^{[27, 65]}.

Citation absorption

The real influence of a source on the content of the answer, not merely the presence of a link ^{[32, 39]}.

Faithfulness

Whether the generated statement actually matches the source it cites ^[39].

Structured data

Markup that helps machine understanding of content in Google Search ^[68].

AI features (Search)

Google features that interact with web content inside answers ^[65].

Frequently asked questions

Does structured data guarantee a place in the AI answer?

No. Structured data helps machine understanding of content in Google Search, but it is not a guarantee of inclusion — a technical precondition, not a lever of certainty ^[68].

Does GEO answer "only from my site"?

No. A RAG answer is retrieval plus the model’s parametric memory; a source raises the odds but does not determine the answer ^[24].

How does a citation differ from absorption?

A citation is the fact of a link; absorption is the source’s real influence on the content of the answer. A correct answer does not guarantee correct attribution ^{[32, 39]}.

Is the passage more important than the page?

Not exactly. Many retrieval/RAG scenarios work at passage level, so a self-contained block helps ^[27]. But Google requires no chunking — a page can be understood whole, and content is written for the audience ^[65].

Sources (E-E-A-T)

01 · Aggarwal et al., 2024, ACM SIGKDD

GEO as a formalized task.

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24 · Lewis et al., 2020, NeurIPS

RAG: retrieval + generation.

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27 · Khattab & Zaharia, 2020, SIGIR

ColBERT: retrieval at passage level.

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32 · Gao, Yen, Yu, Chen, 2023, EMNLP

Generating text with citations and claim support.