Guide to terminology
Answer engine optimization, generative engine optimization, LLM SEO, LLMO and AI SEO all describe the same practice: making a brand more likely to be named and cited in the answers that ChatGPT, Perplexity, Gemini and Google AI Overviews generate. The differences between the five names are differences of emphasis and vocabulary, not of method. The work behind each label is the same work.
This page explains where each term came from, who uses it, what is genuinely different and worth distinguishing, and which word to use when you are writing, hiring or buying.
Every one of these terms was coined by someone describing the same problem from where they happened to be standing. A search marketer named it after search. A researcher named it after the model. A product team named it after the interface.
| Term | What it emphasises | Who tends to use it | Where it came from |
|---|---|---|---|
| AEO — answer engine optimization | The output: an answer, delivered once, with no ranked list beneath it | Agencies and in-house search teams; the term most often used commercially | Extends the older idea of optimising for featured snippets and voice answers |
| GEO — generative engine optimization | The mechanism: the answer is generated rather than retrieved | Academic and technical writing; increasingly, agency positioning | Coined in research literature on influencing generated answers |
| LLM SEO | The system being optimised for: a large language model | Practitioners explaining the work to people who already know SEO | Informal, adopted from developer and SEO community usage |
| LLMO — large language model optimization | The same as LLM SEO, formalised into an acronym | Tool vendors and technical marketers | Back-formation from LLM SEO, following the SEO acronym pattern |
| AI SEO | The category: search, but with AI in it | Buyers and generalist writers; the phrase most people reach for first | Plain-language shorthand rather than a coined term |
A useful test: ask a provider to describe what they do without using any of the five names. If the description that comes back is "we measure which AI answers name the client, find out which sources those answers are built from, and go and improve those sources", then the label was never load-bearing.
Naming disputes usually resolve because one word wins. That has not happened here, and the demand data makes the split visible. We pulled US monthly search volumes from Google Ads data in August 2026 for the whole family of terms:
| Term | US monthly searches |
|---|---|
| answer engine optimization | 2,400 |
| ai seo agency | 1,300 |
| ai search optimization | 1,300 |
| llm seo | 880 |
| geo agency | 590 |
| chatgpt seo | 390 |
| chatgpt optimization | 170 |
| generative engine optimization | no measurable volume |
| ai visibility, ai search visibility, aeo agency | no measurable volume |
Two things stand out. The first is that "generative engine optimization" — the term most used in articles about this subject, including by people selling the service — returns no measurable search volume, while "llm seo" and "geo agency" both have real demand. The long formal name is what the industry writes; it is not what buyers type. The second is that "geo agency" has demand while the phrase it abbreviates does not, which means a meaningful share of those searchers may be looking for something else entirely: geography, local search, or geotargeting. The abbreviation collides with an established word.
Naming collisions are not only a marketing problem. Engines make the same mistake with brand names. In one of our own measurement runs, an engine answered a sportswear question about Puma with advice about the Ruby web server of the same name. This is why prompt design is part of the method rather than an afterthought — a question that does not pin down the category returns an answer about a different category, and any measurement built on it is worthless.
These figures come from audits we run ourselves. If you would rather have your own numbers than ours, apply for a free audit on one brand — real prompts, real answers, across all four engines.
The five names are interchangeable. Two distinctions underneath them are real and worth keeping straight.
The meaningful variation is not between the labels but between the surfaces. Google AI Overviews reaches roughly two billion users a month, appears on roughly 25-30% of informational searches, and is the only genuinely location-aware surface of the four. In our own audit runs, that location sensitivity is decisive: the query "best hair transplant clinic" issued from Lisbon returned an AI Overview naming local clinics, while the identical query from London returned no AI Overview at all. Natural native phrasing triggered an AI Overview where a stiff translated template returned nothing.
Citation behaviour varies too. In a full audit of a global consumer brand — 232 answers across three engines, every citation logged — 92% of cited sources were third-party pages rather than the brand's own site. That figure was 100% for Gemini, 98% for Perplexity and 80% for ChatGPT. Any method that treats the four engines as one surface will optimise for the average of four different behaviours.
One audit of a global consumer brand: 232 answers across three engines, every cited link classified. Measured by BrandsNode, August 2026.
Third-party pagesThe brand's own site
This is the distinction that actually matters, and it gets lost while people argue about acronyms.
| Traditional SEO | AEO / GEO / LLM SEO | |
|---|---|---|
| Target metric | Ranking position and clicks to the site | Being named and cited inside an answer |
| Main lever | The brand's own pages | Third-party sources the engine reads |
| Result delivered to the user | A list of links to choose from | One synthesised answer, often with no link followed |
| Stability | Rankings move gradually and can be tracked daily | Repeated identical prompts return different brands 40-60% of the time month to month |
| How success shows up | Referral traffic in analytics | Branded search, direct visits and shortlist inclusion |
The last row is where most programmes are misjudged. Of the 196 answers in that audit that named the brand, 157 did not cite the brand's own site, and none of the 232 answers contained a contact address. The engine hands the user a name to remember, not a link to click. A client measuring only AI referral traffic will conclude that nothing happened.
Whatever it is called, the method has the same four parts.
No provider using any of the five names does materially different work from this. Where providers genuinely differ is in how many engines they measure, how often they re-run each prompt, whether the prompt set stays fixed between months, and whether they will show you the raw answers behind the numbers.
Unsettled vocabulary is not a harmless quirk. It has a specific commercial effect: buyers cannot compare providers who use different words for the same thing.
A marketing director collecting three proposals — one for AEO, one for GEO, one for AI SEO — has no way to tell from the documents whether they are looking at three approaches or one approach described three ways. The natural response is to compare on price and confidence, which rewards whoever writes the boldest claim rather than whoever measures most rigorously. Some providers understand this perfectly well. Presenting standard work under an unfamiliar acronym makes it look proprietary, and an acronym with no search volume behind it is hard for a buyer to research independently.
The defence is straightforward. Ignore the label and compare the method: which engines, how often, how many runs per prompt, fixed prompt set or not, raw evidence available or not, and what exactly ships each month. Those five questions make three differently-named proposals directly comparable. We cover the full version of this in our guide to how agencies sell AI search visibility.
One thing no label changes: nobody can guarantee placement in an AI answer. Content, entity records and mentions raise the probability that an engine names a brand. They do not control the output. A provider guaranteeing a position is either misunderstanding the mechanism or misrepresenting it, and that is true under all five names.
Yes. Generative engine optimization and answer engine optimization describe the same practice: increasing the chance that a brand is named and cited in AI-generated answers. GEO emphasises that the answer is generated; AEO emphasises that the output is a single answer rather than a ranked list. The measurement and the execution are identical.
It is real, and it is not a rebrand. The target metric is different — being named inside an answer rather than ranked in a list — and so is the main lever. In our audit of 232 answers across three engines, 92% of cited sources were third-party pages rather than the brand's own site, which puts most of the work off-site where classic SEO puts most of its work on-site.
Because it is a term the industry writes rather than one buyers type. Google Ads data for August 2026 shows no measurable US volume for the full phrase, while "llm seo" records 880 monthly searches and "geo agency" records 590. Formal coinages spread through articles and conference talks; search demand follows the words people already use.
The label should not decide it. Compare the method instead: which of the four engines they measure, how often, how many times each prompt is run, whether the prompt set stays fixed between months, whether they will show you the raw answers and cited URLs, and what specifically ships each month. Those answers are comparable across providers regardless of what each one calls the service.
Only indirectly. Engines match meaning rather than exact strings, so a page that explains the practice clearly can be cited for questions asked with any of the five names. What helps is covering the alternatives explicitly on the page, so that the connection between the terms is stated rather than assumed.
We will run a full audit on one brand using the method described here: an agreed prompt set asked across ChatGPT, Perplexity, Gemini and Google AI Overviews, several runs each, with every answer and cited source logged. You receive the prompt set, the named-rate and position, the citation log and a prioritised fix list — in your own branding if you are an agency.
Apply for a free AI visibility auditOne brand, no cost, no card, no obligation. We reply with the audit or with an honest reason it would not tell you anything useful.