LLMO (large language model optimization)
LLMO (large language model optimization) is the practice of improving how large language models, such as those behind ChatGPT and Gemini, describe and recommend a brand, by publishing accurate, consistent information both in the data models learn from and in the pages they retrieve when answering.
What is LLMO?
LLMO treats the language model itself as the thing to optimize for. A model answers a question about a brand from two places: what it absorbed during training, which can be months or years old, and what it retrieves from the live web at the moment of answering. LLMO works on both. The training side is slow and indirect: keep your own pages, directory listings, review profiles and press coverage stating the same current facts, so the next model version learns an accurate picture. The retrieval side is faster: give engines that search the web a clear, quotable page for each question your buyers ask.
In practice the work looks more like entity hygiene than keyword work: one consistent name and description for the company and each product, facts stated plainly rather than implied, and the same facts corroborated by third-party sources a model is likely to trust. How much any single source shifts a model is rarely visible from outside, which is why LLMO is judged by its outcome: what the engines actually say about you.
Example
A company notices that an AI assistant describes it using a product name it retired two years ago. It publishes a clear, current description of its products on its own site and asks the directories and review sites that mention it to update their listings. The engines that retrieve live pages pick up the new name once they re-crawl those pages; the ones answering from training data catch up only when their model is refreshed.
LLMO vs GEO vs AEO
The three labels describe the same work from different angles, and most teams use them interchangeably. The difference is what each one puts in the foreground.
| Term | Stands for | Puts the emphasis on |
|---|---|---|
| LLMO | Large language model optimization | The model: what it believes about a brand, from its training data and from what it retrieves |
| GEO | Generative engine optimization | The engine: how generative systems such as AI chat assistants and AI search compose a response |
| AEO | Answer engine optimization | The outcome: being named and cited in the answer a person receives |
If you want one rule: say AEO when you mean the result, GEO when you mean the systems, and LLMO when the point is the model's own understanding of your brand. The longer comparison is in GEO vs AEO vs LLMO.
Why it matters
Some answers rely mostly on what a model learned during training, which can lag behind your current products and positioning. Others rely on live retrieval. Consistent, accurate information across your site and the third-party sources that mention you helps in both cases, and it is the only part of a model's picture of you that you control.
How Stellarcast handles it
Stellarcast measures the outcome LLMO works toward: whether each AI engine it tracks names and cites your brand for the questions your buyers ask, per market, with a P25-P75 range, and which competitor was named when you were not. That gives LLMO work a before and an after.
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Get your free visibility auditRelated terms
- Generative engine optimization (GEO)
- Answer engine optimization (AEO)
- Unlinked brand mentions
- llms.txt
- GEO vs AEO vs LLMO: the AI-search alphabet soup, explained
- Deutsch: LLMO (Large Language Model Optimization)
- 日本語: LLMO（大規模言語モデル最適化）
- Français: LLMO (optimisation pour les grands modèles de langage)
- Español: LLMO (optimización para grandes modelos de lenguaje)