AEO vs GEO: two names for the same shift?

Answer engine optimization is the practice of structuring content so answer engines can extract and cite it directly: lead with a self-contained answer, mark it up with FAQ and other schema, name entities clearly, and back claims with quotable specifics. The term covers any surface that answers a question without a click — answer boxes and voice assistants as much as AI chat — which is why it slightly predates the current AI-search wave. For the full definition, see what is AEO; for how Meev runs it as a workflow, see the answer engine optimization page.

Generative engine optimization is the practice of optimizing content so generative AI engines — the ones that write an answer rather than return a list of links — include and cite your brand in what they generate. The framing is LLM-first: GEO asks how a model retrieves, synthesizes, and attributes sources, and optimizes for being one of the few citations an answer carries. The tactics — answer-first structure, extractable formatting, clear entities, citation-worthy authority — are the same ones AEO prescribes. Read more on the generative engine optimization page.

The honest version of this table is mostly a column of near-duplicates. The real differences are emphasis and lineage, not method:

Mostly because the industry mints labels faster than practices diverge. AEO was already in circulation when “answer engines” meant featured snippets and voice assistants, so when AI chat products started answering buyer questions directly, the existing term stretched to cover them. GEO arrived later, coined around generative engines specifically and popularized in part by research on how generative engines select sources. Neither term has a standards body behind it, and you'll see the same work filed under LLM SEO, AI SEO, and AI visibility too. The vocabulary is still settling; the underlying shift — answers replacing links — is not.

Strip the labels away and AEO and GEO practitioners run the same four-step loop:

1. Track AI visibility. Ask the engines the questions your buyers ask and record whether you're mentioned and which pages get cited — engine by engine. A free AI visibility checker gives you a baseline in about a minute; an AI visibility tool keeps tracking it continuously.
2. Fix extractable content. Restructure existing pages so an engine can lift the answer: self-contained opening statements, FAQ and structured-data schema, explicit entity names, concrete numbers.
3. Publish answer-first. Ship new pages that lead with the direct answer to a real buyer question, then elaborate — the shape engines extract from.
4. Earn citations. AI engines lean on a handful of trusted third-party pages per topic; getting your brand onto those pages compounds visibility beyond your own domain.

No. The engines you're optimizing for — ChatGPT, Claude, Gemini, Perplexity, Grok, Google AI Overviews, AI Mode, and DeepSeek— don't care what you call the practice; they care whether your content is the easiest credible thing to extract and cite. Pick the workflow, not the acronym: get a baseline, fix and publish extractable content, earn citations, and re-measure. If you're weighing these terms against classic search instead, see AEO vs SEO— that comparison has real differences in surfaces and metrics, where AEO vs GEO mostly doesn't.