What is LLMO (Large Language Model Optimisation)?

What does LLMO mean?

LLMO stands for Large Language Model Optimisation: the work of making sure that language models such as ChatGPT, Gemini or Claude know a company, describe it correctly and name or recommend it in relevant answers.

A language model knows about a company in two ways:

• From training: whatever was in the training data is built into the model. That only changes when a new model is released.
• From web search: many assistants search the web while they answer and read current pages. New or updated pages can often show up in answers within days or weeks.

LLMO works on both. In the short term, what a search finds is what counts. In the long term, what counts is what is said about your company widely and consistently across the web.

What do GEO and AEO mean?

GEO (Generative Engine Optimisation) and AEO (Answer Engine Optimisation) describe the same task as LLMO from a different angle: GEO from the system that generates the answer, AEO from the answer that appears instead of a list of links.

GEO comes from research. The term goes back to a study by Aggarwal and colleagues, first published in 2023 and presented at the KDD 2024 conference. It describes generative engines as systems that pull together information from several sources and summarise it with a language model. In the study’s tests, GEO methods raised visibility in such answers by up to 40%, according to Aggarwal et al. (2024).

AEO starts from the answer. It targets systems that give a direct answer rather than a list of links. That applied to featured snippets in Google search and to voice assistants, and today applies above all to AI answers.

Other labels are in use too, such as AIO (AI Optimisation) and AI SEO. There is no agreed definition: according to Wikipedia (2026), practitioners, vendors and publications use the terms in different ways.

LLMO, GEO, AEO and SEO compared

The four terms differ mainly in what they look at. The work behind them overlaps heavily.

Term	Stands for	Point of view	Goal	Typical metric
SEO	Search Engine Optimisation	the search engine	your page ranks high in the results and gets clicked	rank, impressions, clicks
AEO	Answer Engine Optimisation	the direct answer	your content supplies the answer that is shown or read out	how often your page supplies the answer
GEO	Generative Engine Optimisation	the system that generates answers	your company is named in AI answers and cited as a source	share of answers that name or cite you
LLMO	Large Language Model Optimisation	the language model	the model knows your company and places it correctly	share of answers that name you, and whether the description is right
AI visibility	not a method but the outcome	the measurement	shows how far LLMO, GEO and AEO are working	mention, recommendation and citation shares with a margin of error

In practice LLMO, GEO and AEO mean almost the same work. AI visibility is what you measure at the end. How that works is explained in What is AI visibility?.

Where do LLMO, GEO and AEO overlap?

LLMO, GEO and AEO overlap in nearly every practical step. Four building blocks are always part of it:

1. Access: the crawlers of the AI services are allowed to read your pages. If robots.txt or a firewall locks them out, any other work on the website does little good.
2. Clear facts: name, location, offer, prices and audience are stated in plain sentences on your website, and the same everywhere.
3. Answers to the smaller questions: AI services often split one question into several searches. Google calls this a ‘query fan-out’, according to Google Search Central (2025). Pages that answer those smaller questions directly stand a chance of being read and cited.
4. Confirmation by third parties: directories, associations, trade media and reviews that say the same about you as your website does.

The differences are a matter of emphasis. People who say LLMO tend to think of what the model learnt in training. People who say GEO tend to think of the sources an AI search draws on. People who say AEO think of the shape of the answer. For a company, the label matters less than what is done and how it is measured.

How does LLMO relate to SEO?

LLMO builds on SEO but does not replace it. According to Google’s guide (2026), from Google Search’s point of view, optimising for generative AI search is optimising for search as a whole, and therefore still SEO. Google says you do not need new machine-readable files, AI text files or special markup to appear in Google Search and its AI features. An llms.txt file neither helps nor harms you there.

LLMO goes beyond SEO in three ways:

• Other engines: ChatGPT and Claude use their own search systems. A good Google ranking does not mean they will name your company.
• Third-party sources: an AI reads many pages and ends up naming only a few firms. What directories and media write about you can flow straight into the answer.
• Measurement: instead of a rank, what counts is the share of many answers, because the same question does not get the same answer every time.

What helps with LLMO, and what does not?

The four building blocks above help: access, clear facts, answers to the smaller questions and confirmation by third parties. These shortcuts, on the other hand, achieve little:

• Repeated keywords: using a term ten times on a page does not make it clearer, to people or to language models.
• Hidden instructions to the AI: text meant only for machines is an attempt to deceive and puts your credibility at risk.
• llms.txt as a shortcut: according to its own guide, Google Search does not use it. The foundations come first.
• Content that only repeats: Google’s guide (2026) advises against simply recycling what others have already written. Your own data, prices and experience are what only you can provide.

How do you measure whether LLMO is working?

You measure LLMO through AI visibility: the share of answers that name your company, recommend it or cite it as a source. Because answers vary from one run to the next, a single answer is never a result.

That is why anewera asks the same questions several times a day on four engines: ChatGPT, Gemini, Google AI Overviews and Claude. Failed answers are kept and reported. Every share comes with its margin of error, the range in which the true value probably lies. A change only counts once it is larger than that range. The details are on the method page.

What this looks like from zero is shown in our live case study, ‘Grow in Public’. Measurement has been running since 6 October 2026; there are no results yet.

What is different about LLMO in Switzerland?

Switzerland adds three things: several languages, its own sources and data protection.

Terms. When we looked through Swiss search results in October 2026, agency pages mostly used GEO in their titles and LLMO less often. Many companies simply talk about AI visibility. They usually mean the same thing.

Languages. Google’s AI Mode has been available in Switzerland since 8 October 2025 in German, French and Italian, according to Netzwoche (2025). A hotel study reported by Netzwoche (2026) shows that a company can be very present in German answers and barely appear in French ones. LLMO for Switzerland therefore means content and measurement in every language your customers use, English included.

Swiss sources. In a study of 98 Swiss German SMEs, local.ch was among the sources cited for 63% of the firms examined, search.ch for 24% and comparis.ch for 23%, according to Netzwoche (2026). Complete and consistent listings there are part of LLMO work.

Data protection. LLMO needs no personal data about your customers. But whatever is public on your website, a model can read and repeat. So check which personal data appears on your public pages. For measurement, know where the raw answers and analyses are stored. anewera stores them in Zurich.

How anewera does this work for companies in Switzerland is described on our GEO agency Switzerland page.