GEO / AI search

LLMO (Large Language Model Optimisation)

Also known as: LLMO, Large Language Model Optimisation, LLM optimisation, LLMO tools

LLMO stands for Large Language Model Optimisation and describes the work of ensuring that a language model finds, understands and uses your own content as a source. The term emerged in 2024 and has since competed with GEO (Generative Engine Optimisation) for the same meaning.

The difference lies in the perspective, not in the activity. GEO starts from the search engine — from ChatGPT Search, Perplexity or the AI overviews in Google, that is, from systems that answer a question and link to sources while doing so. LLMO starts from the model — from what is contained in the training data, which terms a model associates with a brand and how it extracts facts from a text.

In practice the measures overlap almost entirely: clean structure, named entities, verifiable statements, structured data, the citability of individual passages. Anyone doing GEO is doing LLMO — and vice versa.

Why there are two terms for the same thing

Both terms arose independently in 2024, because two different groups were describing the same problem. GEO comes from search engine optimisation: an academic paper from 2023 coined the name and examined which textual characteristics increase the probability of being cited in a generated answer. LLMO comes from the AI corner and stresses that behind the answer there is a model, not an index.

The choice of words reveals the origin. Those coming from SEO usually say GEO; those coming from the product or developer world tend to say LLMO. AEO (Answer Engine Optimisation) and AIO (AI Optimisation) are also in circulation for the same field.

For practical purposes this means: do not be impressed by the term. If one provider sells LLMO and another GEO, that is not a difference in the product but one of vocabulary. Ask instead what is actually being measured.

What LLMO emphasises, what GEO emphasises

There is one nuance that does make the distinction useful:

GEO concerns the moment of answering. A system searches the web live, reads a few pages and builds an answer with sources from them. What counts here is whether your page is found and selected at the moment the question is asked. That can be measured — by asking the question and checking who is cited.
LLMO also concerns the model's knowledge. A model often answers without a web search, drawing on what was in its training. Whether your brand is correctly recorded there cannot be influenced at short notice and is hard to measure directly — only indirectly, by asking the model without a web search and checking the answer.

This second level is the reason LLMO survives as a term of its own: it describes something that GEO does not cover.

What ”LLMO tools” are — and what they are not

The same tools are listed under LLMO tools as under GEO tools, AI visibility tools or generative engine optimisation software. Four names, one market.

The tools cannot sensibly be distinguished by the term, but by three questions:

Is it measured or estimated? An honest tool actually puts the question to ChatGPT, Perplexity and the other systems and checks whether your own domain appears in the answer. Some providers extrapolate from rankings instead.
Is the denominator stated? ”38 citations” means nothing without an indication of how many checks it came from. If the number of phrases or models checked changes, the rate changes without anything having changed on the website.
Does optimisation sit alongside it? A pure measurement tool tells you that you are not being cited. It does not tell you why.

Example from practice

Example: two providers are competing for the same agency. One calls its product an ”LLMO platform”, the other a ”GEO suite”. The agency compares the data sheets and finds the same building blocks in both: store prompts, check the answers of several models, count citations, place competitors alongside.

The difference only becomes apparent with the third question from the section above: one delivers only the measurement, the other additionally says which pages are structurally not citable. For the purchasing decision the term was therefore worthless, while the question of optimisation was decisive.

Frequently asked questions

Is LLMO the same as GEO?

In practice, yes. Both describe the work of being found, understood and cited by language models, and the measures overlap almost entirely. The difference lies in the perspective: GEO starts from the answering search engine, LLMO from the model and its training knowledge. Choosing one of the two terms means choosing a linguistic community, not a different method.

Which term will prevail?

That is open. As of August 2026, GEO is searched for more often than LLMO in the German-speaking SEO environment; in the English-speaking product world LLMO is more strongly represented. Alongside them, AEO (Answer Engine Optimisation) and AIO are in circulation. For content it is worth choosing the main term and naming the others as synonyms — then both sides will find you without four almost identical pages being created.

Do I need a separate LLMO tool alongside my SEO tool?

Only if your SEO tool does not actually query the answers of the language models. The decisive difference is not the name of the product, but whether real queries are sent to ChatGPT, Claude, Gemini, Perplexity and the AI overviews — or whether a figure is extrapolated from rankings. The latter does not answer the question.

Can I influence what a model has learned about my brand?

Only slowly and only indirectly. Training knowledge arises from what was on the web at the time of training — no short-term measure has any effect there. What can be influenced is the second level: what a model finds when it looks at the live web to answer a question. That is precisely where GEO measures take effect, and precisely where success can also be measured.

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Last updated: 2026-08-20 · Browse all glossary entries