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LLM SEO is the practice of optimizing content so large language models retrieve, understand, and reproduce it accurately when answering questions. It is a loose umbrella term. In most usage it means the same thing as generative engine optimization; some practitioners use it more narrowly for the model-facing side — how content is parsed and represented — as distinct from how it is discovered.

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The two paths into an answer

A model produces information about a brand either by searching the live web at answer time (retrieval) or from what it absorbed during training. Mentions come from the model’s training data and move slowly; citations come from live retrieval and respond to content — see AI visibility.

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What LLM SEO changes about writing

The writing rules are the same as for GEO — front-load the fact, write self-contained sections, name the subject, state limits, keep URLs stable. See how to write for AI answers.

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What it does not do

LLM SEO does not let you control what a model says about you. It improves the odds that when a model looks for information, it finds an accurate, well-structured source rather than a competitor’s page or an outdated third-party summary.