AEO vs GEO vs SEO vs LLMO: the differences, defined

SEO, GEO, AEO, and LLMO are four names for closely related work: making sure the right people and machines find and recommend your business. SEO optimizes to rank in traditional search results. GEO optimizes to be named inside AI-generated answers. AEO optimizes to be the direct answer to a question. LLMO optimizes how large language models represent your brand. The acronyms overlap heavily, and most of the arguments about them are about labels, not substance.

Still, the distinctions are worth getting right, because they point at genuinely different surfaces and slightly different tactics. Here's each term defined, where they converge, which label is winning, and what actually changes in your day-to-day work.

What is SEO?

SEO (Search Engine Optimization) is the practice of optimizing a website to rank higher in traditional search engine results like Google's list of blue links. It's the oldest and most established of the four, with decades of accumulated practice around keywords, on-page relevance, technical health, and backlinks.

The unit of value in SEO is a ranked page: you target a query, shape a page to satisfy it, earn authority through links, and compete for a position in the results. The scoreboard is rank and organic traffic. SEO isn't going anywhere — billions of queries still resolve to a ranked list — but it now shares the results page with AI summaries that can absorb the click before anyone scrolls to your listing. For the full comparison, see GEO vs SEO.

What is GEO?

GEO (Generative Engine Optimization) is the practice of optimizing how your brand appears inside AI answer engines like ChatGPT, Perplexity, and Google AI Overviews — the way SEO optimizes for traditional search. Instead of ranking in a list, the goal is to be one of the names the engine writes into its synthesized answer.

The unit of value in GEO is a named mention inside an answer, and the dominant lever is third-party consensus: the same facts about you, stated consistently across the sources the model trusts. You can't climb to position one because there is no list — the engine reads the web, decides what's true about your category, and names the options it can corroborate. GEO is the broadest of the AI-era terms because it covers every generative engine, and it's the label PromptHawk uses because it maps cleanly onto how these engines actually behave.

What is AEO?

AEO (Answer Engine Optimization) is the practice of structuring content so it can be lifted directly as the answer to a specific question — in featured snippets, voice assistants, and AI answers alike. It predates the generative-AI wave: AEO grew up around Google's featured snippets and voice search, where the prize was being the answer rather than a ranked option.

In practice, AEO is mostly a content-shape discipline: phrase a heading as the exact question, put a concise, self-contained answer in the first sentence beneath it, and make it trivial to extract. That structure helps snippets, voice, and generative engines simultaneously, which is why AEO and GEO overlap so much. The difference is scope — AEO is about answer-shaped content wherever it's consumed; GEO is about brand visibility specifically inside generative engines.

What is LLMO?

LLMO (Large Language Model Optimization) is the practice of influencing how large language models understand and represent your brand — both in live retrieval and, over time, in what they encode about you. It's the newest and least standardized term, and it's often used interchangeably with GEO.

Where people draw a line, LLMO tends to emphasize the entity layer: whether the model recognizes your brand, associates it with the right category and attributes, and describes it accurately even without live search. That leans on consistent, corroborated information across the web — the same signals that feed how ChatGPT recommends brands. Some also use "GAIO" (Generative AI Optimization) for the same idea. Treat LLMO as GEO with the emphasis on brand entity understanding rather than per-query citation.

Where do these terms overlap?

They overlap far more than the acronym debates suggest, because they rest on a shared foundation. Clean, crawlable, well-structured content helps a page rank, get lifted as an answer, and get cited by a generative engine at the same time. Real authority — being a trusted, recognized source — lifts you everywhere. And answer-shaped writing, the AEO core move, is exactly what generative engines find easiest to quote.

The shared base means the SEO work you've already done isn't wasted; it's the platform the newer disciplines build on. The divergence is only in the winning move on top: SEO leans on links and rankings, AEO on answer structure, GEO and LLMO on cross-source corroboration and entity consistency. You optimize the shared foundation once, then point the specific tactics at whichever surface matters for your buyers.

Which term is winning?

GEO is currently the most-used umbrella term for AI-era visibility work, with AEO close behind and often used for the content-structure slice specifically. SEO remains the parent discipline and the label most businesses still budget under. LLMO and GAIO exist but haven't reached the same adoption. Honestly, the label matters less than the work.

Term	One-line definition	Primary surface	Winning move
SEO	Rank in traditional search results	Google's blue links	Keywords, on-page, backlinks
GEO	Get named inside AI answers	ChatGPT, Perplexity, AI Overviews	Third-party consensus
AEO	Be the direct answer to a question	Snippets, voice, AI answers	Answer-shaped content
LLMO	Shape how LLMs represent your brand	The model's entity understanding	Consistent, corroborated identity

Pick the label your team understands and get on with the actual work: healthy technical SEO, answer-shaped content, consistent facts, and corroboration on the sources engines trust. Whatever you call it, the way to know it's working is to measure whether the engines name you — start with the free AI-visibility check.

Frequently asked questions

What's the difference between GEO and SEO?

SEO optimizes to rank in a list of search results; GEO optimizes to be named inside an AI-generated answer. SEO's unit of value is a ranked page and its main lever is links; GEO's is a named mention and its main lever is corroboration across trusted third-party sources.

Is AEO the same as GEO?

They overlap but aren't identical. AEO is about structuring content to be lifted as the direct answer anywhere — snippets, voice, and AI answers. GEO is specifically about brand visibility inside generative engines. Answer-shaped content (the AEO move) is one of the main tactics GEO relies on.

What does LLMO mean?

LLMO stands for Large Language Model Optimization: influencing how models understand and describe your brand, with an emphasis on the entity layer — whether the model recognizes you and associates you with the right category and attributes. It's often used interchangeably with GEO.

Which acronym should I actually use?

Whichever your team and market understand. GEO is the most common umbrella term for AI-era visibility, AEO for the content-structure slice, and SEO remains the parent discipline. The label matters far less than doing the underlying work well.

Do I have to choose between SEO and GEO?

No. They share a foundation — crawlable, high-quality, authoritative content helps both — and buyers use both surfaces. Keep your SEO healthy and layer GEO on top; optimizing for one doesn't automatically win the other, but the base work serves them together.