Answer engines/Generative engines/Docs design
AEO vs GEO: Two Different Machines Are Reading Your Docs
An estimated 69 percent of searches now end without a click, and AI Overviews already appear on about 83 percent of informational queries, which means two different kinds of machines are reading a page for two different reasons. Answer-engine optimization serves the one that extracts a fact and stops; generative-engine optimization serves the one that synthesizes a longer answer from several pages at once.
Two kinds of machine
Why Does It Matter Which Machine Is Reading?
An extraction engine and a generative engine are solving different problems even when the same product sits behind both. Extraction is a lookup: find the one paragraph that answers this exact question and surface it, often with no click at all given that roughly 69 percent of searches already end that way. Generation is closer to research: read several sources, reconcile them, and write a new answer that cites some subset of what it read.
Optimizing only for extraction produces short, disconnected answer blocks that read well in isolation but say nothing about how concepts on a site relate to each other, which hurts the generative case. Optimizing only for generation produces long, well-connected prose that a strict extractor has to work harder to parse into one clean answer. Both are achievable on the same page if the structure is deliberate rather than accidental.
What each engine rewards
What Does Each Engine Actually Reward?
AEO rewards isolation: a complete, correct, tag-free answer that does not depend on context from elsewhere on the page. GEO rewards consistency: the same facts, the same numbers, and the same terminology used the same way across every page a generative engine might draw from when it builds a synthesized response.
For API docs specifically
How Should This Change the Way Documentation Gets Written?
Write each endpoint description so it answers, in one sentence, what the endpoint does and what it returns, the way a tool-selecting agent needs, then let the request and response examples carry the detail. That single sentence is the AEO answer block; the surrounding docs, error codes, and glossary links are what keep the GEO picture consistent.
See why AI platforms cite different sources for the research this rests on, or the glossary for how the terms on this page are defined consistently across the rest of the site.
Questions, answered plainly
Is AEO just SEO with a new name?
No. Traditional search optimization is built around ranking a page so a human clicks it. AEO is built around a page surviving a much shorter interaction: an extraction engine pulls one fact or one paragraph and never sends a click at all. The page has to be correct and complete in isolation, because there may be no second chance for a click to fix a bad first impression.
How is GEO different from AEO in practice?
A generative engine is not looking for one paragraph to extract. It is drawing from several sources at once and writing a new answer, which means it rewards pages that are internally consistent with the broader body of content on a topic, cite real numbers, and do not contradict what other credible sources say about the same subject.
Can one page satisfy both an extraction engine and a generative one?
Yes, and that is the practical goal. A page with a tag-free, plain-text answer right after each heading satisfies an extraction engine, while consistent facts, named sources, and dated publication satisfy a generative engine building a synthesized answer. Neither requirement conflicts with the other.
Does this apply to API documentation specifically?
It applies more, not less. Documentation is read by coding agents constantly, and an agent selecting a tool needs an extraction-grade answer to "what does this endpoint do" and a generation-grade level of consistency across every page describing the same API. Vague endpoint descriptions cost real integrations, not just search visibility.
See both disciplines in one API's docs.
The documentation is written to answer a single-question lookup and to stay consistent page over page for a longer synthesized read. Accounts are onboarded in launch order; API keys are issued at activation.