LLM SEO

LLM SEO is a colloquial term for the practice of making content easier for large language models to retrieve, parse, and cite correctly when generating answers. It covers technical steps like clear site structure and crawlable pages as well as content-level steps like unambiguous claims, defined terminology, and up-to-date facts that reduce the chance a model misrepresents a brand.

The term borrows familiar SEO vocabulary because many underlying mechanics carry over: crawlability, structured data, and authoritative backlinks still influence whether a model’s retrieval layer finds a page at all. But the endpoint is different. Traditional SEO optimizes for a ranked position on a results page, while LLM SEO optimizes for being selected as source material and paraphrased faithfully inside a generated answer.

Because model providers rarely disclose exactly how retrieval and citation decisions are made, LLM SEO remains more empirical than traditional SEO: practitioners test changes, watch whether mention or citation rates shift, and iterate rather than follow a fixed checklist. Consistency, factual accuracy, and being cited by sources the model already trusts tend to matter more than any single on-page trick.