Generative Engine Optimization: What the Terms Mean

Generative engine optimization (GEO) and answer engine optimization (AEO) are names for the work of getting your brand or pages named or cited in AI answers. Neither has a standard definition. The companies that publish one draw the line in different places. Google says the work is still SEO. The KDD 2024 paper titled “GEO: Generative Engine Optimization” tested one narrow thing. This page maps the words. It isn’t a how-to: what is known about tactics is in how to rank in ChatGPT: what is actually known.

Who uses which word

We opened one page for each label on 6 October 2026. Most of these publishers sell something in the same space, as Bevizio does, so read each line as that company’s own definition.

The first three describe work you do to your content. The fourth is a measurement, a number you read off afterward. Peec AI’s documentation defines its version as the “Percentage of AI responses where your brand appears”, so even the measurement has more than one formula; the AI visibility explainer sets them side by side.

Where the labels split

The practice labels don’t sort neatly. A Microsoft Advertising guide for retailers expands AEO as “Answer/Agentic Engine Optimization”, which it says “Optimizes content for AI agents and assistants (like Copilot or ChatGPT)”. It defines GEO separately, as optimizing “for generative AI search environments (like LLM-powered engines)”. Profound’s AEO definition and Semrush’s GEO definition both name ChatGPT and Perplexity, plus Google. So the same engines sit under different labels, and one acronym has two expansions.

Semrush says “GEO isn’t replacing SEO—it complements it”. Profound says AEO “complements SEO rather than replacing it”. So both vendors present their label as an addition to SEO, not a different field.

What Google says

Google’s guide to generative AI features on Google Search, last updated 10 July 2026, has a section called “What about ‘AEO’ and ‘GEO’?” It calls them terms “you may see used to describe work specifically focused on improving visibility in AI search experiences” and adds: “From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.” It also says you don’t need new machine-readable files or AI text files to appear in Google Search. And “You don’t need to write in a specific way just for generative AI search.”

The guide is about AI Overviews and AI Mode on Google Search, so it doesn’t settle what applies to ChatGPT, Claude or Perplexity. Those companies’ own documentation is covered in why AI engines name your competitor instead of you.

What the KDD 2024 paper tested

The paper says its authors “dub” their framework Generative Engine Optimization. We make no claim about who used the words first. What they ran:

• A benchmark of 10,000 queries (8,000 for training, 1,000 for validation, 1,000 for testing) from nine source datasets, across 25 domains.
• Nine ways of rewriting a source page, tested on the 1,000 test queries with a simulated engine: the top five Google results fed to gpt-3.5-turbo, five answers per query, five random seeds.
• Two measures of how visible a page is in an answer: a position-adjusted word count, and a subjective score a model assigns.
• A second check on Perplexity.ai with 200 queries. Perplexity doesn’t let a user name source URLs, so the authors uploaded the source text as files and had it answer only from them.

The strongest methods were adding quotations, statistics and citations; the figures are in the evidence review, not repeated here. The limits are what matter for the label. The paper measured visibility of a rewritten page inside an answer, not whether a brand was named, and it did not follow a live brand over time. Its authors say they “didn’t evaluate how GEO methods affect search rankings” and that the methods “may need to adapt over time as GEs evolve”. The result also depends on the measure: keyword stuffing scored 17.7 against a no-change baseline of 19.3 on the position-adjusted word count, but 20.2 against 19.3 on the averaged subjective score (the paper’s Table 1).

The authors also write that “traditional SEO methods are not directly applicable to Generative Engines”. Google’s 2026 guide takes the opposite view for its own features. They describe different systems at different times, so in our view neither settles the other.

Where Bevizio sits

Bevizio is a measurement tool, not a GEO or AEO service. It asks AI engines the questions your buyers might ask and reports how often your product is named, who is named instead, and which sources the answers used. It doesn’t rewrite pages or publish content. Answers come through a data provider and can differ from what a person sees in the app, as the how we measure page explains.

We agree with Google that this work sits inside SEO, and that is the reason to measure it. A ranking report shows where your pages sit in a list of links. An assistant’s answer isn’t a list, so whether it names you is a separate question, and it needs its own regular check, the same way rankings do.

What a count can show is whether the number moves after you change something. It can’t show why, and one change rarely stands alone. Because answers vary from run to run, the count only means something over many asks, as why we ask the same question 8 times a week explains. To see a first reading, the free check runs one real question across ChatGPT, Perplexity and Google AI Overviews with no account.