Three vendor calls in a month, three different acronyms, and three demos that looked broadly the same. One deck said AEO, one said GEO, one said the whole thing is just AI SEO and you already have the team for it. You are being asked to buy in a vocabulary that has not settled, and the differences you are hearing may be real distinctions or may be positioning. This post is a map: what each term means when someone uses it carefully, where the terms genuinely diverge, and where the divergence is mostly branding.
No acronym gets crowned at the end. Practitioners using any of these words can be doing serious work, and the choice of label is a weaker signal of quality than what a person can tell you about their method.
The four terms you'll hear most, defined
Each definition below is written to be quotable on its own. Where a term has no settled definition, that is stated rather than papered over.
AEO (Answer Engine Optimization). AEO is the practice of structuring content and information so that it can be extracted and reused as a direct answer by a system that answers questions rather than returning a list of links. The term predates the current wave of assistants and was originally used for featured snippets, voice assistants, and question-answering surfaces. Its meaning has stretched to cover AI assistants, which is why two people using it may be describing different eras of work.
GEO (Generative Engine Optimization). GEO is the practice of influencing whether and how a brand or document is represented in the output of a generative system that synthesises an answer from retrieved sources. The term entered circulation through academic work on optimising content for generative engines, and has since been adopted commercially with looser meaning. It carries an unfortunate collision: in the wider marketing world, "geo" means geographic. Expect confusion in any room containing a local-search practitioner.
LLMO (Large Language Model Optimization). LLMO is used to describe work aimed at how large language models represent a brand, including what the model produces without retrieving anything. This term has no settled definition. Some users mean influencing what a model recalls from training, which is slow and largely outside anyone's control. Others use it as a straight synonym for GEO. Because the two readings imply very different work on very different timescales, it is worth asking which one a speaker means before agreeing to anything.
AI SEO. AI SEO is an umbrella label covering both search-engine work adapted for AI-generated answer surfaces and, confusingly, the use of AI tools to do conventional SEO work. Those two meanings are unrelated and are routinely conflated in the same sentence. When a vendor says AI SEO, the useful question is whether they are selling visibility in AI answers or an AI-assisted version of the work you already buy.
A fifth phrase you will meet is AI visibility, which is a measurement term rather than an optimisation one: it describes whether and how a brand appears in AI-generated answers, without claiming any particular tactic. That is the phrase this site leans on, for reasons set out below.
What they have in common
Strip the labels off and the underlying job is largely shared. All four terms are attempts to name the same shift: a class of systems now assembles an answer instead of returning a list, and being present in that assembled answer is a distinct outcome from ranking on a results page.
Practically, most people working under any of these labels end up doing an overlapping set of things:
- Making the facts about an entity, its products, and its category consistent and unambiguous across the public record.
- Making sure content is technically reachable and readable by the crawlers and retrieval systems involved.
- Writing content in a form that survives being extracted as a self-contained passage rather than only making sense in the context of a full page.
- Earning presence in the third-party sources, comparisons, and communities that a retrieval system is likely to reach for.
- Measuring the result by sampling answers repeatedly rather than checking once.
If a vendor's list of activities under any acronym looks materially different from that, the interesting conversation is about the difference, not the label.
Where they genuinely differ
There are real distinctions hiding in the vocabulary, and they are worth keeping.
| Term | Primary surface implied | Mechanism emphasised | Realistic time to see change |
|---|---|---|---|
| AEO | Answer boxes, voice, question-answering features, and now assistants | Extractability of a direct answer | Weeks, where the surface retrieves live |
| GEO | Synthesised answers from generative systems with retrieval | Presence in retrieved sources and how they get summarised | Weeks to months |
| LLMO | Model output, including with no retrieval at all | What a model recalls, plus retrieval depending on speaker | Model generations, and largely uncontrollable |
| AI SEO | Ambiguous by design; often all of the above | Ambiguous; sometimes means AI-assisted conventional SEO | Depends entirely on the reading |
The distinction that carries the most weight is between work aimed at retrieval and work aimed at recall. Retrieval-side work touches things you own or can influence, and it has a feedback loop measured in weeks. Recall-side work has no submission path and no measurable feedback loop on any planning horizon a marketing team uses. A term that blurs the two, which LLMO frequently does, makes it easy to sell slow-or-impossible work at retrieval-speed expectations. The mechanics behind that split are covered in how ChatGPT decides which brands to recommend.
The second real distinction is surface. AEO's history means it often includes search-engine answer features, which behave differently from a conversational assistant with session context. If a proposal treats those as one surface with one number, that is a substantive problem regardless of which acronym is on the cover.
Which ones are vendor positioning rather than practice
A fair test: does the term describe a mechanism, a surface, or an outcome that can be defined without reference to a product? Or does it mainly exist to make a category feel new?
By that test, AEO and GEO both describe something real, even though usage is loose. Each points at an identifiable surface and an identifiable mechanism, and each can be defined without naming a vendor.
LLMO is closer to the line, not because the underlying idea is fake but because the term is used for two incompatible things and the more ambitious reading, influencing what a model has internalised, is the one that is hardest to verify and easiest to sell. Any pitch built on that reading should be asked directly how the effect would be measured and over what horizon.
AI SEO is the least informative of the four, because its ambiguity is load-bearing. It lets a proposal sound adjacent to something you already fund while leaving the actual scope unstated.
None of this makes a practitioner who uses a given term unserious. Vocabulary in a young field gets adopted from whoever spoke loudest at the time, and plenty of careful people use whichever word their clients recognise. The signal that separates rigour from repackaging is not the acronym. It is whether the person can tell you which surfaces they measure, how many runs they use, which model versions and dates the numbers come from, and what they will not claim.
Terminology instability is a buyer risk with a concrete cost. When two proposals use different words for the same work, and the same word for different work, you cannot compare them on scope, price, or outcome. The defence is to make every proposal restate its scope in mechanism and surface terms before you compare anything.
That defence is cheap. Ask each vendor to fill in the same four blanks: which assistants and surfaces, which mechanism they are targeting, how the result is measured, and what they explicitly do not claim. Comparable proposals appear immediately, and the acronym stops mattering.
The vocabulary this site uses, and why
RankRift uses AI visibility as the umbrella and treats it as a measurement term. The reasoning is straightforward: measurement can be defined precisely, and the tactic vocabulary cannot be yet. "Whether an assistant names your brand, in a defined prompt set, across defined models, on defined dates, with stated variance" is a sentence that can be checked. "Optimised for generative engines" is not.
When a mechanism needs naming, this site says retrieval, recall, or tool use rather than an acronym. When an outcome needs naming, it distinguishes being mentioned in an answer from being cited as a source, because those are different events with different causes. The metric vocabulary that follows from that split is laid out in which metric answers which question, and the full term-by-term reference lives in the full glossary.
We use AEO and GEO where a reader is likely to search for them, and define them when we do. We do not use LLMO as a load-bearing term, because we cannot define it in a way that stays stable across the people who use it.
Frequently asked questions
Is GEO just SEO with a new name? It overlaps heavily with technical and content SEO, and teams that do those well have a large head start. What is genuinely new is the measurement problem: answers vary run to run, so you sample rather than check, and position on a page is replaced by presence in a synthesis.
Which term should I put in my job title or scope of work? Use whichever term the people funding your work already recognise, then define the scope in mechanism and surface terms in the first paragraph. The definition is what protects you later, not the label.
Do I need a separate vendor for this? Not necessarily, and that is a real answer rather than a hedge. If your existing team already handles entity consistency, technical retrievability, and third-party coverage, the missing piece is usually measurement rather than a new discipline.
Will these terms settle? Probably some of them, and there is no way to predict which. Write scopes and reports so they survive a vocabulary change: name the surface, the mechanism, and the metric, and treat the acronym as a label rather than a definition.
In Plain English
Four acronyms are circulating for roughly one job: being present in answers that assistants assemble. AEO and GEO describe something real with loose usage. LLMO has no settled meaning and is used for two incompatible things. AI SEO is an umbrella that can mean either AI-answer work or AI-assisted ordinary SEO. The label matters far less than whether the person using it can state their surfaces, mechanism, measurement, and limits.
This is most useful before a vendor call or an internal scoping conversation. Use it to convert three incomparable proposals into three comparable ones. The next step is to write down the four blanks, send them to everyone pitching you, and compare the answers rather than the vocabulary.
Want the term-by-term reference with formulas and limits? Explore the glossary.
Primary-source verification
Checked July 28, 2026. The operational and factual guidance above was reviewed against the primary references below. Provider features, prices, policies, interfaces, jurisdictions, and account-specific results can change; recheck the controlling first-party document and your own account or measured data before acting.
- Google Search: AI features and your website - How Google describes eligibility, preview controls, and appearance in its AI search features.
- Google: common crawlers and special-case user agents - Current Googlebot and Google-Extended identities and controls.
- OpenAI: overview of OpenAI crawlers - Current GPTBot, OAI-SearchBot, and ChatGPT-User descriptions and publisher controls.
- Anthropic: web-crawler controls - Current ClaudeBot, Claude-SearchBot, and Claude-User descriptions and controls.
- Perplexity: crawler and user-agent documentation - Current PerplexityBot and Perplexity-User descriptions and controls.
- Aggarwal et al.: GEO, Generative Engine Optimization - The primary paper that introduced and evaluated the GEO framing.