LLMO is Large Language Model Optimisation: structuring your content, entity signals and wider presence so that large language models cite or recommend your business. In practice it describes the same work as GEO. The two terms emphasise different halves of one problem.
This page exists mainly to settle the vocabulary, because the proliferation of near-identical terms is costing buyers money. Four labels, one discipline, and a market where the naming is used to justify separate invoices.
The Four Terms, Disambiguated
| Term | Stands for | What it emphasises | Where you hear it |
|---|---|---|---|
| LLMO | Large Language Model Optimisation | The model itself: training data, retrieval, citation behaviour | Technical audiences, developers |
| GEO | Generative Engine Optimisation | The generative search products built on models | UK agencies and buyers, most traction |
| AEO | Answer Engine Optimisation | Winning the direct answer specifically | Precise when you mean snippets and AI Overviews |
| AI SEO | AI Search Engine Optimisation | The umbrella over all of it | What buyers actually type |
The practical actions behind all four are the same. Make the entity resolvable. Make content extractable. Make claims specific. Earn independent corroboration. Mark it up properly. Measure citations rather than positions.
If an agency tells you LLMO and GEO are different services needing different budgets, that is a commercial structure rather than a technical distinction, and it is worth naming as such.
Which Should You Use?
Whichever your audience uses.
Use GEO with UK agencies and marketing buyers. It has the most traction and the least ambiguity in this market.
Use AI SEO in anything meant to be found by search, because it is what people type. This site uses it deliberately on the AI SEO page for that reason.
Use AEO when you specifically mean winning the direct answer rather than being cited in a longer one. It is a genuinely useful distinction and worth keeping precise. See what is AEO.
Use LLMO with technical audiences, where “large language model” is the natural frame.
Then stop thinking about it. The vocabulary is unsettled and will stay unsettled for a while. The work does not change with the label, and time spent arguing about acronyms is time not spent on corroboration. Fuller treatment: LLMO vs GEO vs AEO, the terminology problem solved.
What the LLMO Framing Genuinely Adds
Having said the terms are interchangeable, the LLMO framing does foreground two things the others underplay, and both matter.
Training data versus live retrieval
A model answers in one of two ways, and they fail differently.
From training. A fixed snapshot, absorbed at a point in time. It cannot be edited, it has no date, and it updates only when the provider retrains. This is why a business sometimes sees an assistant state something about it that has been wrong for two years.
From live retrieval. The model searches, fetches current pages, and can cite them. This is the half you can influence in a reasonable timeframe, and it is where nearly all practical work happens.
Knowing which one is failing you changes what to do. If an assistant describes you wrongly when answering from memory but correctly when it searches, your live retrieval is fine and your training footprint is thin, which corrects slowly and on somebody else’s schedule. Why doesn’t my business show up in ChatGPT covers how to tell them apart.
Retrieval-augmented generation, and its two gates
RAG is the mechanism behind live retrieval, and it has two gates people conflate.
Gate one is retrieval. Your page must be reachable and fetchable. Blocked crawlers, JavaScript-dependent content, or simply not surfacing for the underlying query all mean you never enter the candidate set.
Gate two is selection. Being retrieved is not being used. From the candidates, the model picks what to cite, favouring material that answers directly, states specifics, and comes from a source it can identify.
Most businesses fail at gate one and assume they failed at gate two, so they write more content when they should be fixing access and structure. Detail: what is retrieval-augmented generation · do AI crawlers visit continuously
The Four Signals, Whatever You Call It
Entity authority. Whether a model can confidently identify your business, and whether it associates you with a subject. Two separate failures needing two separate fixes. See what is entity authority.
Content architecture. Whether an answer can be lifted cleanly. Answer in the first sixty words, question-shaped headings, one idea per section, markup matching visible content.
Source credibility. What independent sources say about you, on the domains a model already draws on for your category. The slow half, and the decisive one.
Cross-platform consistency. The same facts everywhere. Inconsistency does not merely fail to help, it gives a model reasons to doubt everything it has assembled about you.
Do Models Differ Enough to Optimise Separately?
Somewhat, and less than the effort of separate programmes would justify. The foundations serve all of them. But the differences are real enough to explain why results arrive unevenly:
- ChatGPT runs three distinct crawlers, governed independently, and answers from training or live browsing depending on the question.
- Perplexity weights recency hardest and draws heavily on forums and community threads. It usually moves first, which makes it a useful early indicator.
- Claude is noticeably more cautious about naming businesses, especially in regulated categories. Corroboration moves it; volume does not.
- Gemini grounds through Google Search, and unlike AI Overviews it genuinely is governed by
Google-Extended. - Copilot grounds in Bing, which most UK firms have never optimised for and which is therefore unusually uncontested.
Diagnose per platform: ChatGPT · Perplexity · Claude · Gemini · Copilot · Google AI Overviews
How to Measure It
Referral traffic is a poor primary measure, because many assistants strip or omit the referrer and the visit lands as direct or unassigned. The better your visibility gets, the more your unattributed traffic grows and the less credit AI gets for it. See why is my unassigned traffic growing in GA4.
Measure citation directly instead:
- Fix a query set of 20 to 50 real buyer questions, before you measure.
- Run them across the platforms that matter, several times, because outputs vary run to run.
- Record whether you are cited and who is cited instead.
- Repeat monthly, same queries, same day.
- Segment by platform. A blended number hides the platform-level detail that is actionable.
What is a good AI citation rate covers what the numbers mean, can you A/B test GEO covers testing properly, and how to measure AI search visibility is the full method.
Where to Actually Start
The terminology argument is the least useful part of this subject. The sequence below is the same whichever label you attach to it.
- Establish what a model currently believes about you. Ask several assistants to describe your business, and ask them the questions your buyers ask. Record who is named instead of you. This is your baseline and it takes an afternoon.
- Separate the two failure modes. If it describes you wrongly from memory but correctly when it searches, your live retrieval is fine and your training footprint is thin. If it cannot describe you at all, you have an identification problem, and that comes before everything else.
- Fix the entity. Consistent naming and descriptors everywhere,
Organizationschema withsameAs, and one canonical page stating the facts. Ours is at /entity-map/ and the schema generator will build the markup free. - Clear gate one. Confirm the crawlers can reach you, that main content renders without JavaScript, and that no firewall rule is quietly returning 403 to AI user agents.
- Make the pages that matter extractable. Answer in the first sixty words. Specifics rather than adjectives. Headings shaped like the questions people ask.
- Then build corroboration, on the sources already cited in your category. This is the slow half and the half that decides contested queries.
Steps one to five are genuinely achievable in-house and cost little beyond attention. Step six is where most firms need help, and it is where the money goes. Honest reading on that choice: can I do GEO myself and how much of my team’s time it takes.
The Fair Criticism
The scepticism about this vocabulary is reasonable and worth meeting head on.
Four terms for one discipline mostly serves vendors. It creates the impression of specialist services where there is one practice, and it lets agencies sell three retainers for work that is eighty per cent shared. That is a legitimate complaint and this page exists partly to defuse it.
What is not fair is concluding that because the naming is messy the underlying change is not real. Take a query you rank well for, run it in Google, then ask the same question in ChatGPT or Perplexity. If you are on page one and absent from the answer, you have just observed the gap the words are pointing at. It takes a minute and it settles the argument better than any acronym does.
More: is GEO just a buzzword · is SEO dead because of AI
LLMO: Common Questions
What is LLMO?
LLMO is Large Language Model Optimisation: structuring your content, entity signals and wider presence so that large language models reference, cite or recommend your business. In practice it describes the same work as GEO. The two terms emphasise different halves of the same problem, LLMO the model and GEO the search experience built on top of it.
Is LLMO different from GEO?
Not meaningfully in what you do. The distinction people draw is one of emphasis: LLMO points at the model itself, including training data and retrieval, while GEO points at the generative search products built on models. Every practical action, entity clarity, extractable content, structured data, third-party corroboration, is identical. Anyone selling them as separate services with separate retainers is selling a pricing model.
Which term should I use?
Use whichever your audience does. GEO has the most traction among UK agencies and buyers, AI SEO is what most people actually type into a search box, AEO is precise when you specifically mean winning the direct answer, and LLMO is more common among technical audiences. The vocabulary is unsettled and arguing about it is a poor use of anyone’s time.
Is LLMO a real discipline or a buzzword?
The name is new and the behaviour change underneath it is real and measurable. What is fair criticism is the proliferation of near-identical terms, which mostly serves vendors rather than buyers. Judge a practitioner by what they measure rather than which acronym they prefer: if they cannot show you citation tracking, the label changed and the practice did not.
Does normal SEO cover LLMO?
It covers roughly eighty per cent of it. Entity clarity, technical accessibility, structured data and credible third-party mentions all serve both. The gap is the remaining twenty per cent: writing so an answer can be extracted cleanly, making claims specific enough to quote, and measuring citation rather than position. That gap is small in effort and decisive in outcome.
How do I measure LLMO success?
On a fixed query set, run monthly across the platforms that matter, recording whether you are cited and who is cited instead. Citation share segmented by platform, not a blended number. Referral traffic is a weak proxy because many assistants strip the referrer, so treat any referral figure as a floor rather than a count.
Can LLMO work for local businesses?
For specialist local businesses, yes. Models handle specific queries well, so a local firm with a genuine specialism can be named where a generalist cannot. Proximity-driven queries such as nearest or open now still belong to maps. The winnable local ground is the query with a qualifier attached.
What is the difference between LLMO and prompt engineering?
They point in opposite directions. Prompt engineering is about writing better inputs to get better outputs from a model, which is a skill for using AI. LLMO is about making your business the thing a model reaches for when somebody else prompts it. One is about using the tool, the other about being found by it.
Where to Go Next
The category, explained: what is GEO · what is AI SEO · what is AEO · AI Overviews
Do it yourself: the 35 GEO questions · free schema generator · free llms.txt generator · free AI Visibility Score
Have it delivered: MarGen is a UK GEO agency, AI SEO agency and AEO agency, with published pricing, a published method and case studies carrying real numbers.