What is LLM Optimization (LLMO)?
LLM Optimization (LLMO) is an umbrella term for measures aimed at making large language models know a brand, product or website, describe it correctly and recommend it in answers. The term is used largely as a synonym for GEO and AEO.
The acronym puts the language model itself in focus rather than the search interface. Behind it are two different routes by which a model can learn about a brand. The first is training data, texts collected before the model's knowledge cutoff. The second is retrieval at answer time, where the system searches the web while answering and reads sources.
The two routes behave differently. What is missing from training data cannot be added in the short term. Retrieval at answer time reacts to current pages, as long as they are reachable and findable. For answers that show sources, the second route is usually the one that decides.
Anyone offered measures under the LLMO label should ask which of the two routes they are meant to work on and how the effect is measured.
What it means for your website
For a website the first question is whether a model can read the page during retrieval at all. If a server blocks the crawlers of OpenAI or Anthropic, no text on the page helps. The second question is whether the model names the brand for a fitting question, and that can only be answered with repeated real queries.
Related terms
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Sources
This entry contains no figures and relies on generally documented terms.
Terms help you understand. Whether AI crawlers can reach your site is something you measure.
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