What is Large Language Model Optimization (LLMO)?
LLMO is one of several names for getting picked up by AI. It sits next to GEO and AEO, and the difference is the angle: LLMO is about being chosen as a source. This guide defines it plainly and shows how it fits with the rest.
What is Large Language Model Optimization?
Large Language Model Optimization (LLMO) is the practice of optimizing your content and authority so large language models like ChatGPT, Claude and Gemini select your brand as a source when they generate answers. The focus is the citation: being the source the model pulls from, not just a name it mentions.
Where the broader term GEO covers all of getting featured by AI, LLMO narrows in on one lever: which sources a model trusts and retrieves. It is the supply side of an AI answer, the documents and pages the model draws on before it writes a word.
How is LLMO different from GEO and SEO?
SEO optimizes for search rankings and clicks. GEO optimizes for being featured in AI answers overall. LLMO is the slice of GEO focused on source selection: being the page a model cites. In practice the tactics overlap, but LLMO puts authority and citability first.
A page can be mentioned by name without being cited as a source, and cited as a source without strong search rankings. LLMO targets that second outcome: clean, authoritative, well-structured content that a model is comfortable attributing to.
What makes an LLM choose your content as a source?
Models favour sources that are clearly written, specific, well-structured and consistently referenced across the web. A self-contained passage that answers a question, names its subject, and sits on a site with broad third-party mentions is far more likely to be pulled in than vague prose with thin external presence.
- Concise, self-contained passages a model can lift and attribute.
- First-party data and specifics, not generic claims.
- Your brand named in the passages you want cited.
- Consistent mentions across independent sources and forums, the strongest off-page signal for being trusted as a source.
How do you actually do LLMO?
Measure which questions cite competitors and not your site, publish authoritative, well-structured content that answers exactly those questions, name your brand in it, add schema, and build mentions across trusted third-party sources. Then re-measure, because source selection shifts with coverage and recency.
That is the same gap-driven loop GEO uses, aimed specifically at the citation. Citanto runs it end to end, and tracks not just whether you're mentioned but whether your own domain is cited as a source.
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Frequently asked questions
Is LLMO the same as GEO?
They overlap heavily. GEO (Generative Engine Optimization) is the umbrella for getting featured by AI. LLMO (Large Language Model Optimization) is the part focused on being selected as a source by the model. Most teams use the terms interchangeably; the distinction is one of emphasis.
Is LLMO different from AEO?
Yes, by angle. AEO (Answer Engine Optimization) focuses on the questions you show up for. LLMO focuses on being the source a model cites. They're complementary slices of the same goal: being part of the AI answer.
How do I measure LLMO?
Track your citation rate, how often your own site is cited as a source in AI answers, separately from your mention rate, how often your brand is merely named. A gap between the two (named but not cited) is the signal that you have an authority problem LLMO addresses. See AI visibility for how both are measured.