AEO vs GEO: are they the same thing?

Answer Engine Optimization (AEO) is the practice of getting named and cited inside AI-generated answers. When a buyer asks ChatGPT, Claude, Gemini or Perplexity to recommend something, an answer engine returns a short list of names, and AEO is the work of being one of those names rather than a rival.

The term "answer engine" is deliberately broad. It covers any system that hands the buyer a finished answer instead of a list of links: AI chat assistants, AI overviews in search, and voice answers all qualify. AEO is about winning the recommendation, wherever the answer is generated.

Generative Engine Optimization (GEO) is the practice of getting your brand surfaced and recommended by generative AI engines, the large language models behind ChatGPT, Claude, Gemini and Perplexity. The "generative" part points at how the answer is made: the model writes a fresh response rather than returning a stored list.

Read closely, GEO and AEO emphasise different things: GEO names the technology doing the answering, AEO names the job of being in the answer. In day-to-day use, that distinction collapses. The engines people mean by "generative" are the same engines people mean by "answer", so the two terms end up describing the same practice.

Treat AEO and GEO as synonyms. A few practitioners reserve GEO for generative chat engines specifically and use AEO as the wider umbrella, but no two people draw that line the same way, and nothing you actually do changes depending on which word you picked. The deliverables are the same.

Whatever you call it, the work happens on your own site and comes down to four levers:

• Authoritative, answer-shaped content. The biggest lever by far. Publish the page that directly answers the buyer's question, in the format the question implies, a "best X for Y" query wants a guide that names the field, not a brochure.
• Structured data. Schema.org JSON-LD (Organization, FAQPage) states your facts in a form the model can read. FAQPage here is for AI extraction, not Google rich snippets, and never put an invented rating in it.
• Crawlability. A sitemap plus a robots.txt that allows the AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended) is load-bearing. An llms.txt file is low-cost hygiene that hints at your priority pages.
• Owned profiles. Claim and complete the entries you control, G2, Trustpilot, LinkedIn, Crunchbase, Wikidata, so the model has accurate facts to draw on. You can't buy editorial coverage, so don't chase it.

A handful of related acronyms float around the same space. None of them changes the plan; they're mostly different framings of the same shift:

• SEO (Search Engine Optimization), the older discipline of ranking in a list of links. It overlaps heavily with AEO, because the pages an AI reads and cites are often the pages that already rank. The goal differs: SEO wants the link, AEO wants to be the named recommendation.
• LLMO (Large Language Model Optimization), another name for the same work, framed around the language models themselves. Effectively a synonym for GEO.
• AIO (AI Optimization, sometimes Artificial Intelligence Optimization), the broadest catch-all, used loosely for getting your brand to show up well across AI surfaces.

The honest takeaway: the field hasn't settled on one word yet. Pick whichever term your team and your buyers understand, then spend your energy on the four levers above, that's where the recommendation is actually won.

Timelines vary by engine, which is worth knowing whatever you call the work. Retrieval engines (Perplexity, and the web-search modes of ChatGPT and Gemini) reflect new pages within days of crawling them, so some wins are fast. A base model's training memory only shifts when it's retrained, so other wins build over time. Publish, then re-check the answers on a cadence to watch the recommendation move.