Settle the entity
One clear name, one clear description, one address. Actively ruling out confusion with similarly named things.
Large language model optimisation explained
Work on what the model finds.
LLMO concerns the picture of you that exists across the web rather than your website. The offer sits on the service page.
Definition
What the term describes.
Large language model optimisation describes measures intended to influence what a language model knows about an entity and which sources it draws that from. The subject is facts, sources and the clear demarcation of the entity itself.
A model knows your company from two directions: from what sat in the training data, and from what it retrieves at the moment of the question. Only the second can be influenced from outside, and that happens through sources.
So LLMO is the least technical and the slowest-acting of the four disciplines. Mentions do not appear on request, and models pick up new sources with a delay.
Components
What gets worked on.
Resolve contradictions
Where two versions exist on the web, a model picks one. That choice does not always land on the right one.
Build sources
Text not written by you that still talks about you. Trade media, associations, directories, programmes.
Structured facts
One connected graph with stable anchors instead of scattered fragments.
Access
A clear statement per provider in robots.txt, plus an llms.txt. Both consistent with whatever else is said about AI use.
Measurement per model
Citations recorded separately per system. Only the breakdown shows where something worked.
Limits
What is not possible.
There is no channel for feeding content into a model. Offers promising that either describe something else or sell something that does not exist.
Training data cut-offs explain why some systems repeat outdated details. Nobody outside influences that. What can be influenced is the part retrieved live.
And the main caveat: here too the providers disclose nothing. What stands here rests on observation across repeated measurements.
Common questions
About LLMO as a term.
Why is the abbreviation LLMO ambiguous?
Because the same four letters also stand for large language model operations, meaning the running of models. Here it consistently means large language model optimisation.
Is LLMO the same as GEO?
No, it is narrower. GEO looks at the whole answer surface, LLMO at the source and fact layer behind it. The comparison sits in the boundaries article.
What is an entity in this context?
A uniquely identifiable thing: this company, this person, this product. Systems work with those rather than with strings, which is why clean demarcation matters so much.
How long until a model knows something new?
For live-retrieved sources days to weeks. For model knowledge, until the next version, and the providers decide when that lands.
Do we need Wikipedia?
Helpful, not necessary. Many companies do not meet the notability criteria, and a bought entry gets found out. Wikidata is often the more practical route for structured facts.
Does the link count or the mention?
Both, with different weight. A mention without a link can be valuable when it confirms your facts. That is what separates LLMO from classic link building.
What if something wrong about us is online?
Correct it at the source rather than at the model. Almost every wrong claim in an answer traces back to a page that contains it.
Can models be influenced with volume of text?
Not usefully. Volume without substance produces no citation and tends to damage the picture, because claims start contradicting each other.
Does LLMO apply to people as well?
Yes, and particularly there. Personal brands often suffer from confusion with people of the same name, and clean entity work improves that considerably.
What does citation frequency actually count?
How often a given model names the entity across a fixed question set, broken down by the source it leans on. The distribution matters more than the total, and the details sit under metrics.
Next step
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