LLM Optimization (LLMO)
Definition: LLM optimization (LLMO) is the work of improving how large language models describe, recommend and cite your brand, both in answers drawn from live web search and in what models learned during training.
Also called: LLMO, large language model optimization, LLM SEO
Updated
Two routes into a model’s answer
A language model can mention you for two different reasons. Either it retrieved one of your pages while answering, through retrieval-augmented generation, or your brand appeared often enough in its training data that the model already “knows” you. LLMO covers both.
Retrieval depends on things you control directly: crawler access, indexing, page quality and how clearly each section answers a question. Training-data presence depends on how widely and consistently you are discussed across the web, and it only changes when a model is retrained, which can take months.
What you can actually influence
- Let search crawlers fetch your pages. OAI-SearchBot, Claude-SearchBot and PerplexityBot feed the live search features. Blocking training crawlers such as GPTBot is a separate decision.
- Keep your name, product descriptions and key facts consistent wherever they appear, including your own site, directories and social profiles.
- Earn mentions on sites that models are likely to read: reviews, forums, industry publications.
Research shows branded web mentions correlated more strongly with AI Overview visibility than backlinks did in an Ahrefs study of 75,000 brands. That is a correlation, and large brands tend to have plenty of both.
A note on terminology
LLMO, GEO and AI SEO are used almost interchangeably. LLMO tends to be preferred by people who think about the model rather than the search interface around it. The GEO vs SEO guide explains where the labels overlap and where the work genuinely changes.