AEO Guide · Terminology
GEO vs SEO, and where AEO fits
Three acronyms, two genuinely different disciplines, one turf war over naming. This page defines generative engine optimization properly, compares it with classic SEO, and settles the GEO vs AEO question as far as it can be settled. Part of the complete AEO guide.
What is generative engine optimization (GEO)?
GEO is the practice of optimizing content so generative AI systems (ChatGPT, Gemini, Perplexity, AI Overviews) retrieve and cite it when generating answers. The term comes from a 2023 academic paper that measured which content changes increased citation likelihood; industry adopted the name for the whole discipline.
The research finding that made the paper travel: additions like statistics, quotations, and citations measurably increased how often content was referenced in generated answers, while superlative-heavy rewrites did not. That result aged well, and it is the empirical backbone of most practical advice in this guide, ours included.
What is the difference between GEO and SEO?
Same as the AEO vs SEO split: SEO optimizes for position in ranked results, GEO for inclusion in generated answers. The fundamentals overlap completely (indexation gates both), but GEO additionally rewards extractable structure, verifiable specifics, freshness, and corroboration across independent sources, because generative engines synthesize rather than list.
If you came here comparing tactics rather than names, the full working comparison lives in AEO vs SEO; every row of it applies verbatim with GEO substituted for AEO. The practical checklist is in how to optimize for AI search.
GEO vs AEO: is there any real difference?
In practice, no: both name the discipline of earning citations from AI systems. The connotation differs slightly. GEO emphasizes the engine type (generative), AEO the output (answers), and AEO's lineage includes the older featured-snippet and voice-search work, so it reads a little broader. Pick one term and be consistent.
We use AEO as the umbrella on this site because clients ask about answers, not architectures, and because the answer-shaped work (question headings, direct answers, FAQ schema) predates generative engines and still matters. When a proposal or an article uses GEO, LLM SEO, or AI search optimization, it is near-certainly describing the same checklist. The model-side view has its own page: LLM SEO.
Which term should you search for or buy?
Judge providers by their checklist, not their acronym. Whatever the label, the work should include indexation on both Google and Bing, open AI crawler access, schema, answer-first content structure, verifiable claims, and listing consistency, plus measurement you can inspect. A provider leading with a proprietary score and no raw evidence is selling the label.
The measurement bar to hold anyone to: verified crawler data (not raw user-agent counts; a third of claimed AI traffic on sites we track fails IP verification, numbers in the pillar guide) and recorded engine answers to a fixed question set. That standard is checkable regardless of what the invoice calls the service. Ours calls it AI search optimization, with published pricing.
Skip the naming debate, check your site.
Whatever you call the discipline, the free audit checks the same foundation: both indexes, crawler access, schema, and llms.txt.