What is Large Language Model Optimization (LLMO)?
LLMO is one of several names for being 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.
By the Citanto team · Updated
What is Large Language Model Optimization?
Large Language Model Optimization (LLMO) is the practice of working on your content and authority so large language models like ChatGPT, Claude and Gemini can select your site as a source when they generate answers. The focus is the citation: being the source the model draws on, not only a name it mentions.
Where the broader term GEO covers all of being featured by AI, LLMO narrows in on one lever: which sources a model retrieves and cites. It is the supply side of an AI answer, the pages the model draws on before it writes a word.
How is LLMO different from GEO and SEO?
SEO works on search rankings and clicks. GEO works on 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 clarity 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: clear, authoritative, well-structured content a model can attribute.
What makes an LLM choose your content as a source?
No vendor publishes the rules. Pages that are clearly written, specific and well structured are easier to extract and attribute, and a brand described consistently on independent sites gives a model more material to draw on.
- Concise, self-contained passages a model can quote 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.
How do you approach LLMO?
Measure which questions cite competitors and not your site, publish authoritative, well-structured content that answers those questions, name your brand in it, add structured data that matches the page, and earn mentions on trusted third-party sources. Then re-measure, because source selection changes over time.
Citanto supports the measurement and on-site content parts of that loop, and reports whether your own domain is cited as a source separately from brand mentions.
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Frequently asked questions
Is LLMO the same as GEO?
They overlap heavily. GEO (Generative Engine Optimization) is the umbrella for being 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 named). A gap between the two, named but not cited, can point to an authority issue worth investigating. See AI visibility for how both are measured.