GEO vs. AEO vs. LLM SEO: what's the actual difference?
By Vladimir Boldyrev · LLMBrandScan · Published July 21, 2026
Three terms, one underlying job, and no standards body to settle the naming fight. If you've seen “GEO,” “AEO,” and “LLM SEO” used interchangeably in the same blog post — that's not sloppy writing, it's an accurate reflection of how unsettled the vocabulary still is. Here's the disambiguation.
GEO (Generative Engine Optimization)
- Origin
- Coined in a 2023 Princeton/Georgia Tech/Allen Institute research paper studying how to improve visibility in generative search results.
- Focus
- The broadest term — brand presence and favorability across any AI-generated answer, on any engine.
- Use it when
- You want the academically-grounded, increasingly industry-standard term for a report, RFP, or job title.
AEO (Answer Engine Optimization)
- Origin
- Predates the generative-AI wave — originally described optimizing for featured snippets, voice assistants, and direct-answer search boxes.
- Focus
- Answer-extraction mechanics: is a specific fact easy for a system to find and lift cleanly.
- Use it when
- You're talking about content structure and answer-extractability specifically, not brand-level visibility.
LLM SEO
- Origin
- An informal, agency-coined synonym — no distinct origin, just SEO vocabulary applied to the LLM surface.
- Focus
- Same as GEO in practice; the name signals 'SEO, but for chatbots' to a buyer unfamiliar with GEO/AEO.
- Use it when
- You're writing for an audience more comfortable with 'SEO' as a category than with newer acronyms.
Why the naming fight barely matters
"LLM SEO" is an informal, non-standardized term for the general effort of making a brand more visible in large-language-model outputs — it is not a distinct discipline from Generative Engine Optimization (GEO) so much as a plainer-language synonym for it, popularized by search marketers applying familiar SEO vocabulary to a new surface. The three terms in circulation — GEO, Answer Engine Optimization (AEO), and LLM SEO — describe overlapping angles on one underlying job: earning accurate, favorable representation when an AI system answers a category question. None of the three has a standards body or formal definition; usage is set by whichever vendors and publications adopt a term first, which is why the same tactic (clear, well-structured, verifiable content) gets marketed under all three names depending on who's selling it. The safer brief names the outcome — more accurate, favorable mentions in AI answers — rather than picking a side in a naming debate no AI lab has weighed in on.
Whichever term ends up dominant, the underlying tasks converge: publish clear, verifiable, well-structured facts about your product; make them easy for a model to find and cite; and measure whether any of it correlates with more mentions. Read our GEO explainer for the definition in full, or AEO myths vs. facts for the answer-extraction side specifically.
The one thing all three actually require
Measurement. Whatever you call the discipline, the only way to know if it's working is to sample real questions against real engines and track whether your mention rate moves — not to trust that a tactic labeled “GEO” or “LLM SEO” works because the label sounds current.
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More AI-visibility guides.
- What Generative Engine Optimization (GEO) Actually MeansThe definition, how it differs from SEO, and why 'optimizing for AI answers' is a different job than optimizing for a ranked list.
- Answer Engine Optimization (AEO): Myths vs. FactsAEO, GEO, and LLM SEO get used interchangeably online. Here's what's actually true about each — and where the myths come from.
- How to Create an llms.txt File (Step by Step)A working llms.txt template, what belongs in it, and an honest read on what it does and doesn't do for AI visibility.
- What Is AI Visibility, and How Is It Actually Measured?The metrics the category has converged on — Visibility Score, Share of Voice, first-mention — explained from first principles.
- How to Improve Your Odds of Being Recommended by ChatGPTSeven concrete, testable levers — and three things that don't work no matter how confidently they're sold to you.
- How Perplexity Chooses Sources to CiteThe mechanics of Perplexity's citation model, what makes a page citable, and how to track whether you're being cited at all.
- Optimizing for Google AI OverviewsWhat triggers an AI Overview, how it differs from classic SGE and organic ranking, and a practical checklist to work through.
- AI Brand Monitoring: Why a One-Time Check Isn't EnoughAI answers drift month to month. What ongoing monitoring actually requires, and the DIY-vs-tool tradeoffs honestly laid out.
- AI Share of Voice: The Formula and a Worked ExampleThe exact formula, a worked numeric example with real arithmetic, and how it maps onto the classic marketing metric it's borrowed from.