LLM optimization, explained
LLM optimization, sometimes called LLM SEO or LLMO, is another name in the family that includes AEO and GEO. The emphasis is on the large language model itself: what it has learned about a brand, and what it finds when it looks for current information.
Those two routes call for different work. What a model learned in training reflects the public web before its training data cutoff, so the influence there is indirect and slow: being described consistently and credibly across many sources over time. What a model finds through live retrieval depends on familiar search factors, such as crawl access, page quality, relevance and authority, and can change much more quickly.
Practical LLM optimization therefore looks like careful SEO with a few additions: clear entity information, content structured so passages can be quoted out of context, consistent facts across profiles and directories, and attention to which third-party sources models cite in a category. It should not involve hidden text, instructions aimed at AI systems or other attempts to manipulate model output. Those tactics are unethical, increasingly detectable and a risk to the brand that uses them.