AEO, GEO, LLMO: What These New SEO Acronyms Actually Mean (And Why They’re Not Just Jargon)
If you’ve opened a marketing newsletter in the last six months, you’ve probably been hit with at least one of these new terms: AEO, GEO & LLMO. Stacked together like a particularly unfriendly game of Scrabble.
It’s tempting to assume someone in marketing got bored and invented three new words for SEO. You wouldn’t be far off, they’re all closely related to SEO, but each brings different uses of Artificial Intelligence. They each describe genuinely different things, and confusing them leads to wasted effort, wasted budget, and the nagging sense that something important is happening without you.
Here’s the plain-English version, with enough depth underneath for anyone who wants to actually do something with it.
First, The Breakdown
- AEO (Answer Engine Optimisation): This is about your content being the answer to queries inputted to Large Language Models (LLMs)
- GEO (Generative Engine Optimisation): This is about your content getting pulled in, as a source, into AI-generated answers.
- LLMO (Large Language Model Optimisation): The newest of the three, it involves influencing what AI models know about you in the first place.
Think of it as a pyramid. AEO is the goal. GEO is the technique. LLMO is the plumbing underneath. If that’s all you needed, you can close the tab; but the detail is where the money is.
AEO: Answer Engine Optimisation
Picture yourself walking into a library and asking the librarian for the best Italian restaurant in town. The librarian doesn’t hand you ten books and suggest you have a read. They just tell you the answer.
That’s what an answer engine does. AEO is about making sure your restaurant is the one the librarian recommends.
Answer engines include ChatGPT, Perplexity, Google AI Overviews (the panels at the top of a Google search), Microsoft Copilot, and voice assistants like Siri, Alexa, and Google Assistant. The shared feature is that the user receives a single answer, no blue links, no list of ten options to pick from. That one shift changes the optimisation game entirely. You’re no longer trying to rank number one in a list. You’re trying to be the source the engine quotes from.
AEO actually predates the generative-AI boom. The term was coined around 2019 for featured snippets and voice search, and it’s been repurposed for the AI era because the underlying goal hasn’t changed: be the answer.
What That Looks Like in Practice
Front-load your answers. When a reader (or an engine) lands on a page, the first forty to sixty words should directly answer the question the page is trying to address. If you start with five paragraphs of context before getting to the point, most extractors will have moved on.
Phrase your headings as actual questions people ask. “Our Pricing philosophy” is vanity. “How much does SEO cost per month?” is AEO.
Use FAQ and HowTo schema. It tells machines explicitly what’s a question and what’s an answer, which makes lifting your content into a reply far easier for them.
Keep paragraphs short enough to be quoted wholesale. And write in plain, declarative English; hedged sentences (“it might be the case that…”) confuse extractors and get skipped.
GEO: Generative Engine Optimisation
Here is where things get more interesting.
Generative engines don’t just retrieve a result. They compose a new one. When you ask ChatGPT to explain mortgage rates, it isn’t finding a single best page and handing it to you, it’s analysing and evaluating dozens of sources into a fresh reply.
GEO is about optimising for those blended answers. You’re no longer trying to be the one page it quotes. You’re trying to be one of the several sources it weighs, and ideally the one it names out loud.
The term comes from a specific place: a 2023 research paper by Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi, titled GEO: Generative Engine Optimization. The authors ran controlled experiments to test which content characteristics actually moved the needle on citation rates inside generative answers. Some of their findings were predictable. Some were not.
What The Research (And Our Own Testing) says works
Quote authoritative sources inside your own content. Counterintuitive, but it holds up: pages that cite others are cited more often themselves.
Add statistics and specific numbers. Generative engines lean towards content that looks verifiable. Round, confident, numerical claims get picked up more than waffle.
Use confident, declarative language. Hedging hurts you. “Research suggests that perhaps…” is cited less than “A 2023 Princeton study found that…”
Include author credentials. Named experts with visible experience and backgrounds are favoured over anonymous copy.
Go deep on the topic. Thin “101” content loses to pages that cover adjacent questions on the same URL, the generative engine wants one source it can lean on for the whole answer, not three it has to stitch together.
Invest in structured data. Organisation, Author, and Article schema make entity extraction cleaner, and cleaner entities get cited more confidently.
If AEO is how you write, GEO is how you earn the model’s trust enough to be included in what it writes.
LLMO: Large Language Model Optimisation
This is the newest of the three, therefore the most misunderstood.
Large language models (the engines behind ChatGPT, Claude, and Gemini) have two ways of “knowing” things about your business. The first is what they learned during training: a static snapshot of the internet, compressed into the model itself. The second is what they look up in real time, using retrieval tools or web browsing.
LLMO is about influencing both.
The training side is a long game. If a model has never seen your brand mentioned during training, it doesn’t know you exist, and no amount of clever on-page work will fix that at answer time. You need to already be in the mix before the question gets asked.
The retrieval side is more immediate. When ChatGPT browses the web to answer a query, or Perplexity pulls citations in real time, your site needs to be both findable and structured in a way the system can actually use.
How to Build Model Memory
Consistent brand mentions across high-authority sites, industry publications, trade press, podcasts with written transcripts. Each reputable mention is a vote that your brand is a real entity worth remembering.
Wikipedia and Wikidata entries: Models lean on these disproportionately. A well-maintained Wikidata entry often does more for your AI visibility than three months of link building.
Consistent descriptions of who you are across your own site, LinkedIn, Crunchbase, and any industry directories you appear in. Contradictory bios make you look like a less reliable entity and models will quietly downweight you.
Content that can be cleanly chunked. Short, self-contained paragraphs, sensible heading hierarchy, clean HTML. Retrieval-augmented systems carve pages up before they use them, pages that carve well get used more.
The pattern underneath all of this: the more predictable and consistent your entity looks across the web, the more confident the model is in mentioning you.
How The Three Actually Fit Together
Here’s a working analogy; you own a restaurant.
LLMO is making sure your restaurant exists in the food critic’s mental map of the city. If they’ve never heard of you, you’re not getting a recommendation, ever. It doesn’t matter how good your carbonara is.
GEO is making sure that when the critic sits down to write their “best Italian in town” guide, the evidence around you: in reviews, photos, awards, chef credentials & specific dishes is strong enough that you make the cut.
AEO is the neat, quotable line on your sandwich board out front “Voted best carbonara in the county”, that the critic can lift straight into the article.
All three run at the same time. You can’t skip one and win the others.
What This Means For Your Business
Short version: if your SEO strategy stopped at “write blog posts and build links,” it isn’t enough any more. Not because those things stopped mattering (they still do), but because the engines deciding what gets surfaced have changed.
Practical first moves, in priority order:
Audit your existing content for extractability. Are your answers front-loaded? Are your headings phrased as real questions? Is your schema in place where it should be?
Test how you’re currently cited. Ask ChatGPT, Perplexity, Gemini, and Copilot the questions your customers would ask and see whether you show up. If you don’t, that’s the gap, and it’s measurable.
Check your entity footprint. Wikipedia, Wikidata, Crunchbase, LinkedIn, industry directories, are you there, and do they all agree on who you are? Inconsistencies quietly cost you visibility.
Publish the deep, specific, well-cited content that generative engines reward. Thin “101” posts are the losers of this shift. Substantive pieces with expert quotes, named data, clear structure, and genuine opinion are the winners.
None of this requires abandoning your existing SEO. AEO, GEO, and LLMO sit on top of good traditional SEO, they don’t replace it. The acronyms themselves will probably consolidate over the next year or two as the industry settles on shared language. What won’t change is the underlying shift: search is moving from “here are ten results, pick one” to “here is one answer, composed for you.”
The businesses that adapt to that first are the ones being quoted. The rest become the ones being paraphrased around.
If you’d like a hand working out where you stand on any of the three, and which is worth your attention first, that’s exactly the sort of thing we spend our days on.
