LLM SEO is the practice of making your content usable by large language models, so that when one answers a question in your field it reaches for your page rather than someone else’s.
If you have read about GEO, you have read about this. The two names describe the same work from different angles, and which one you use mostly signals which corner of the industry you picked it up from.
GEO tends to mean the outcome: getting cited by generative engines. LLM SEO tends to mean the mechanism: writing and structuring for the model itself. Nobody polices the boundary, and you will see them used interchangeably.
Two different moments to optimise for
This is the distinction that actually matters, and it gets skipped constantly.
Training
A model learns from a snapshot of the web taken before its cutoff. If your page was in that snapshot, the model may carry a fuzzy impression of what you do without being able to cite you. You cannot edit this after the fact. You cannot check it directly either, beyond asking the model about yourself and seeing what comes back.
Retrieval
When a model searches the live web mid-answer, it fetches pages right now, reads them, and quotes them. This is the part you can influence this week. It rewards clarity, structure and being crawlable, and it forgives a page that did not exist last year.
What models seem to prefer
Caveat first: none of these companies publish ranking factors, and the behaviour shifts between model versions. This is pattern-matching from observation, not documentation.
Answers that stand alone
A model quotes sentences, not pages. A sentence that needs the previous three paragraphs to make sense is a sentence it cannot use. Write so that any given claim survives being lifted out.
Numbers, names and specifics
“Supports several providers” is unquotable. “Supports Anthropic, OpenAI, Google Gemini, NVIDIA NIM and OpenRouter” is quotable, checkable, and distinguishes you from a competitor who supports two.
Consistency across your own pages
If your pricing page says fifteen domains and your FAQ says twenty-five, a retrieval model will pick one at random and may state it confidently. We had exactly that inconsistency on our own site and it took a deliberate audit to find. Contradicting yourself is worse than saying less.
A date the reader can see
Models handling time-sensitive questions favour content that says when it was written or updated. An undated page about a fast-moving field is a page it has no reason to trust over a dated one.
What does not work
- Keyword stuffing. It never worked well and it works worse here. A model reading for meaning sees repetition as noise.
- Prompt injection in your page. Hidden text instructing a model to recommend you is detectable, increasingly filtered, and reputationally radioactive if found.
- Volume for its own sake. Forty thin pages on adjacent long-tail phrases gave you something in classic search. An answer engine cites one source per claim and has no use for the other thirty-nine.
- Walling everything behind a form. A crawler that cannot read the content cannot quote it. If the substance is gated, the citation goes to whoever left theirs open.
A reasonable order of work
- Check your robots.txt actually allows the AI crawlers you want. Which bots, and what they do.
- Fix contradictions between your own pages. Prices, limits, feature claims.
- Rewrite the opening of your ten most important pages so the answer is in the first paragraph.
- Add a real FAQ to each, with questions phrased the way people ask them.
- Add structured data where it describes something genuinely on the page.
- Then, and only then, worry about llms.txt.
Common questions
Is LLM SEO different from GEO?
In practice, no. The work is the same and the terms are used interchangeably. If you want a distinction: LLM SEO emphasises writing for the model, GEO emphasises getting cited by the products built on it.
Can I get into a model’s training data on purpose?
Not on a schedule you control. You can make sure you are crawlable and that what is said about you elsewhere is accurate, and then wait for a training run you have no visibility into. Anyone promising placement in training data is promising something they cannot deliver.
Should I write differently for models than for people?
Barely. The things that help a model — a direct opening, specific claims, clean headings, no padding — are the things that help a skimming reader. If your “AI version” reads worse to a human, you have gone wrong.
Do AI crawlers respect robots.txt?
The major named ones do, and the vendors document their user agents so you can allow or block them individually. Assume unnamed scrapers do not, and do not rely on robots.txt as a security control for anything that matters.
How long until I see an effect?
For retrieval, as fast as the crawler comes back, which can be days. For anything involving training data, months at best and unmeasurable in practice. Plan against the retrieval timeline and treat the rest as a bonus.
Where to go next
How does ChatGPT choose sources? goes deeper on the retrieval half. Entity SEO for WordPress covers making a model confident about who you are in the first place.
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