AEO, GEO, LLMO, AI SEO — is it all the same thing?

For a buyer, AEO vs GEO vs LLMO is a question about labels, not about different services. Every agency using one of these words is selling the same underlying outcome: when someone asks ChatGPT, Perplexity, Gemini or Google’s AI Overviews a question in your category, your company is named or your page is cited. What differs is where each term came from, which surface it emphasizes, and — the part that matters when you sign — what it lets you measure.

The table below is the one-page version. The rest of this guide explains the rows and ends with the practical question: which word to put in a brief so you get comparable quotes.

Answer engine optimization vs generative engine optimization is mostly a question of dates. AEO is the oldest. The earliest use we know of is a Trustpilot white paper written with Jason Barnard, “The Rise of Voice and AEO”, handed out at BrightonSEO in September 2017; Search Engine Watch covered the term in February 2018 and Barnard presented “A Universal Strategy for Answer Engine Optimisation (Beyond Position 0)” at BrightonSEO that April. The answer engines it had in mind were Google’s featured snippets and the voice assistants of the time. The claim that this is where the term was coined comes from Barnard’s own account; the trade-press date is independent.

GEO has the clearest paper trail. “GEO: Generative Engine Optimization” by Pranjal Aggarwal and colleagues appeared on arXiv in November 2023. It defines a generative engine as a system that retrieves sources and synthesizes an answer with citations, proposes GEO as the discipline of increasing a source’s visibility inside those answers, and tests content changes against its own benchmark. The paper is why most agencies, tools and this site use GEO as the umbrella word.

LLMO has no paper and no founding talk. It shows up in vendor writing to mean optimizing for the model layer: what an assistant says about you when it is not searching the web at all. AI SEO and LLM SEO are what people type; they are useful for finding a vendor and too loose to define a scope.

Most of the work is shared. Server-rendered text a crawler can read, one canonical host (a single address every version of the site redirects to), structured data, a page per buyer question with the answer first, and one consistent description of your company everywhere it appears — any competent AEO, GEO or LLMO program starts there. If we put our own engagement task lists side by side, roughly four fifths of the items would look the same under any of the three labels; that is an estimate from our plans, not a study.

The last fifth is where the label shows. AEO leans on formatting: question headings, short answer passages (we use 40–60 words), FAQ and HowTo structure, schema that mirrors the page. It was built for an extractor that lifts one passage, and it still works for AI Overviews, which draw on the same index; Google’s own documentation states there are no additional requirements or special optimizations for AI Overviews and AI Mode, and no AI-specific files or schema are needed.

GEO adds what a generating model checks before it cites: independent corroboration (other sites describing you the way you describe yourself), original data only you can publish, and freshness. Ahrefs (October 2025) looked at ChatGPT’s 1,000 most-cited pages; of the 564 with a detectable update date, 76.4% had been updated within the previous month. Two caveats from the same report: more than half of that subset is Wikipedia, and the timestamps are unreliable. The GEO paper itself found statistics, quotations and citations of sources moved visibility most on its benchmark; keyword stuffing did not.

LLMO is the hardest to act on directly, because nobody outside the labs decides what goes into training data. What you can do is make your company easy to identify: use one legal name, description and founder biography across your site, LinkedIn, review profiles and structured data. Independent mentions also build up over several quarters. Our guide to how long GEO takes calls this “brand mass”; it develops more slowly than the other two areas.

Measurement is the practical difference between GEO and AEO. Without defined measures, a proposal may simply be a content retainer under a new name.

Use GEO in the brief and define it in one sentence: “being named and cited by AI assistants on the prompts our buyers actually type, measured on a fixed prompt set.” It is the term with a research definition (our guide on what GEO is walks through it), it is the word the specialist agencies and tracking tools we see most often use, and it forces the measurement question early. Add AEO if Google’s answer boxes matter to you, and say so explicitly, because a snippet program and a citation program are scoped and measured differently.

When comparing proposals, look beyond the label. Check the prompt set, which assistants will be tested and how often; the crawler-access work planned for the first thirty days; and what happens if results do not improve. Our guide to questions to ask a GEO agency has the full list. Our GEO pricing guide shows how we specify those commitments.