GEO vs AEO vs LLMO: what they mean, how they differ, and what to do
What is GEO, AEO, and LLMO? Learn how they compare to SEO, what optimize to be cited means, and whether small brands can win.
SEO helps you get discovered and trusted in classic search. AEO focuses on being the direct answer. GEO focuses on being included and cited in AI-generated answers. LLMO is the broad umbrella for optimizing how LLM systems interpret and recommend you. These overlap. The practical work is: be retrievable, be quotable, be validated.
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If you've read anything about AI search lately, you've seen the taxonomy fight: GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), LLMO (Large Language Model Optimization), AI SEO, LLM SEO. Half of it is people describing the same shift from different angles, and half of it is vibes. If you're still getting oriented on AI search itself, start here: What is AI search?. This guide is about the labels and what they mean in practice.
Why everyone argues about GEO vs AEO vs LLMO
Because each term points at a different "surface".
- Some people look at the interface and say "answer engines" so they call it AEO (Answer Engine Optimization).
- Some people look at generative summaries and Citations and say GEO (Generative Engine Optimization).
- Some people zoom out and say "LLMs are everywhere" so they call it LLMO (Large Language Model Optimization).
They aren't mutually exclusive. They're different ways to describe the same reality: users are getting conclusions first, and sources second.
What is GEO?
GEO (Generative Engine Optimization) (Generative Engine Optimization) is the practice of increasing your chances of being included (and ideally cited) inside AI-generated answers.
Think of it like this: in classic search, you fought for a click. In AI answers, you fight to be part of the answer.
If you want to go deeper on the citation side, this guide is the next step: AI citations and URL citation depth.
What is AEO?
AEO (Answer Engine Optimization) (Answer Engine Optimization) is optimizing content so it can be selected as a direct answer to a question.
Historically it was tied to featured snippets and voice answers. In the AI era, it overlaps heavily with GEO, but the vibe is slightly different:
- AEO is "be the best answer block"
- GEO is "be the best answer and a safe source to cite"
What is LLMO?
LLMO (Large Language Model Optimization) (Large Language Model Optimization) is the umbrella term.
People use it to include everything that affects how LLM systems represent and recommend you:
- your public content and brand footprint
- your product docs and help content
- your structured data and consistency
- your presence in third-party sources
If GEO is "visibility in generative answers", LLMO is "visibility across the whole LLM-driven ecosystem".
GEO vs SEO: what's actually different in practice
SEO is still the foundation. If your site is inaccessible, unclear, or untrustworthy, you won't be a great candidate for Retrieval-based systems either.
What changes is what "winning" means.
In SEO you mostly measure rankings and clicks. In GEO/LLMO you care about:
- being selected as a source
- being summarized correctly
- being cited (when citations exist)
- showing up consistently across many prompts
That pushes you toward:
- clearer structure (so the right chunk can be extracted)
- stronger evidence (so it's safe to quote)
- broader validation (so you're trusted beyond your site)
Is GEO legit or a short-lived gimmick?
GEO is legit if you treat it like brand + clarity + trust.
It's not legit if you treat it like a trick.
The durable stuff:
- clean explanations that are easy to quote
- consistent facts across your website and third-party pages
- reputable mentions and reviews
- pages that answer real questions directly
The fragile stuff:
- chasing quirks of one engine
- stuffing pages with "AI SEO" buzzwords
- tactics that make pages worse for humans
What "optimize to be cited" means in practice
It means your content and your brand footprint look "safe" to use as a source.
In practice, Citation optimization usually looks like:
- the page answers the question directly (no vague marketing fog)
- key claims have proof (examples, constraints, sources when needed)
- sections are clearly titled (so the model can pick the right chunk)
- your brand is validated elsewhere (lists, reviews, credible mentions)
If you want measurement language for this, tie it to your confidence framework: AI confidence in LLM-powered search.
Can small brands win? Yes, but here's how
Small brands win by being the most credible specialist, not the loudest generalist.
A good pattern is:
- pick a narrow category or use case
- publish the clearest page on it
- earn a few high-quality mentions in the right places
- keep the facts consistent everywhere
In AI answers, "best cited specialist" can beat "largest brand with generic positioning".
FAQ
Are GEO, AEO and LLMO the same thing?
They overlap. GEO focuses on inclusion and citations in generative answers, AEO focuses on being the direct answer, LLMO is a broad umbrella across LLM-driven systems.
Should I stop doing SEO and focus on GEO?
No. SEO is still the base layer. GEO builds on it by optimizing for inclusion, quotes, and citations.
What's the fastest way to see progress?
Pick one topic you want to own, publish the clearest answer page on it, then earn a few credible mentions pointing to it. Track whether you start appearing in AI answers for a stable prompt set.
Is adding more schema enough to win citations?
Schema helps clarity. But "safe to cite" comes from specificity, evidence, and trust signals.
This guide is updated when the taxonomy evolves. We track how these terms are used in practice, verify definitions against current usage, and revise when the landscape shifts.
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