Classic SEO asks where you rank. LLM SEO asks what the model says when someone describes your category, and nobody types your name.
LLM SEO (also written LLMO, for large language model optimization) is the discipline of influencing that sentence. This guide covers what it is, how it differs from SEO and from GEO, a strategy that survives contact with reality, and a checklist you can run against your own site this week.
Table of contents
What is LLM SEO (LLMO)?
LLM SEO is the practice of shaping what a large language model knows, says and cites about a brand, whether or not the user is on a search engine at all.
The difference from everything before it is the absence of a results page. When someone asks ChatGPT, Claude, Gemini or Copilot for "a GEO agency that works in French and German", there is no position 1 to win. There is a paragraph, three or four names in it, and a reason each name is there. LLM SEO is the work of being one of those names for the questions that matter to your business.
LLMO is not prompt engineering. You do not control the question; the user writes it, and the model rewrites it before retrieving anything.
And it is not "getting into the training data". You cannot meaningfully influence what a model absorbed during pre-training on a timescale that matters to a business.
What you can influence is everything the model reads at answer time (the live retrieval) and the density and consistency of what the open web says about you, which is what shapes the next model as well.
LLMO vs GEO vs SEO: is there a difference?
Honestly, LLMO and GEO name almost the same work. GEO (generative engine optimization) is the older and more widely used term, and it is the one Google, the SEO press and most tooling have settled on. LLMO is used more often by people from the AI side.
Where a real distinction exists, it is one of emphasis:
- GEO tends to describe optimizing for generative search surfaces: AI Overviews, Perplexity, the search modes of the assistants. Retrieval is central.
- LLMO tends to describe optimizing for what the model says from its own weights and memory, including conversations that never trigger a search.
- AEO is the passage-level craft underneath both: making a chunk of your content usable as an answer.
In practice, the same work serves all three, which is why our GEO services do not sell them as separate products. If a vendor charges you separately for GEO and LLMO, ask what is different in the deliverables. In our experience, the same underlying work covers both.
Where models get their opinion of you
Four inputs, in descending order of how much you can move them.
Live retrieval. Most assistants now search before answering anything time-sensitive or specific. This is the most controllable input: it is your site, indexed and retrievable, plus whatever else ranks for the rewritten query. Classic technical SEO applies without modification.
Third-party pages that describe your category. Listicles, directories, comparison posts, review sites, forum threads, trade press. When a model is asked "who are the best X in Y", these are the documents it leans on, because they are the ones that contain the comparison. Most brands ignore this surface entirely, because it sits outside their own CMS and nothing in their normal SEO workflow ever touches it. It is also where a single fix compounds fastest: a good directory entry gets read by every model that indexes that directory.
Consistent facts about your entity. One brand name, one location, one founding date, one description of what you do. And the same version of each on your site, in your schema, on your profiles, and anywhere else you appear. Models resolve entities by corroboration. Contradictions do not get averaged out; they get hedged around, and a hedged brand is a brand that does not get recommended.
Pre-training memory. Slow, opaque, and largely a by-product of the three above accumulating over years. Worth understanding, not worth planning around.
An LLMO strategy that holds up
Start from the questions, not the keywords. Write down the twenty to fifty questions a real buyer would type into an assistant on the way to choosing someone like you. They are longer and more situational than keywords: "who can do technical SEO for a Shopify store selling in three countries" rather than "ecommerce SEO agency". That list is your measurement set for the next year.
Measure before you touch anything. Run each question against the engines your buyers use and record the answer verbatim: who is named, in what order, and which sources are linked. Without a baseline, every later reading is a story.
Fix retrieval first. If your pages are not indexed, are slow to fetch, or hide their substance behind JavaScript, nothing else you do will register. This is unglamorous and it is where most of the first month goes.
Make the substance quotable. Self-contained sections, question-shaped headings, direct answers in the first two lines, tables for comparisons, schema that mirrors the visible text. The AEO guide covers this layer in detail.
Then work off-site, where the comparisons live. Get accurate, current entries in the directories and listicles that already rank for your category questions. Give journalists and analysts something specific enough to quote. Publish data only you have. A claim that appears on three independent domains carries evidential weight. The same claim on one does not.
Re-measure on a schedule. Model answers drift week to week for reasons that have nothing to do with you. A single reading is noise; a monthly series is a signal. We built SEOcrawl's AI visibility tracking to run exactly this loop.
The LLMO checklist
Run this against your own site. Anything you cannot tick is work.
Retrievable
- Every page you want quoted returns 200 to a plain fetch, with its substance in the HTML.
- robots.txt does not block the AI crawlers you want reading you, and you have made a deliberate decision about the ones you do not.
- An llms.txt exists and points at the pages that actually answer questions.
- Sitemaps are current; canonicals are self-referential and correct.
Quotable
- Every H2 is a question or a noun phrase a person would search for.
- The first two sentences under each heading answer it outright.
- No section depends on the one before it to make sense.
- Comparisons, specs, prices and steps are in tables, not prose.
- Organization, Article and FAQPage schema exist and repeat the visible text exactly.
Resolvable as an entity
- Brand name, legal name, location and founding date are identical on your site, your schema, and your main profiles.
- Your services are described with the words your market uses, not internal product names.
- Author and team pages exist, with real credentials, and are linked from the content those people wrote.
- Your Organization schema includes sameAs links to your Wikidata entry, LinkedIn, Crunchbase and any other canonical profile a model can cross-reference.
Corroborated
- You appear in the directories and comparison pages that already rank for your category questions.
- At least one recent third-party page describes what you do accurately.
- Your claims can be checked somewhere other than your own website.
Measured
- A fixed list of buyer questions exists and is versioned.
- Each question is run against the engines on a schedule, and answers are stored.
- Brand search volume and direct traffic are on the same chart as the citation counts.
What LLM SEO services should and should not promise
A vendor can honestly promise to make you retrievable, to make your content quotable, to fix your entity data, to earn third-party coverage, and to measure citation share over time against a baseline.
Nobody can promise a position, because there is none. Nobody can promise that a model will name you for a given prompt, because the same prompt returns different answers on different days and to different users. And nobody can put your brand into a model's weights on request.
If a proposal contains the phrase "guaranteed inclusion in ChatGPT", the useful response is to ask how it would be verified. This work is measurable, but as a rate: over a set of buyer questions, over a period of time, and against a baseline.
Our GEO services page sets out what we actually do and in what order, and the GEO best practices guide is the long version of the method.
Frequently asked questions
What is LLM SEO?
LLM SEO (also called LLMO or large language model optimization) is the practice of shaping what a language model knows, says, and cites about a brand. Instead of winning a position on a results page, the goal is to be one of the three or four names a model gives when someone describes your category without typing your brand name.
Is LLMO the same as GEO?
Almost. GEO, or generative engine optimization, is the older and more widely used term and tends to emphasise generative search surfaces where retrieval is central. LLMO tends to emphasise what a model says from its own memory, including conversations that never trigger a search. The deliverables are the same work, so a vendor charging separately for GEO and LLMO should be asked what differs between them.
Can you get your brand into an LLM's training data?
Not on a timescale that matters to a business, and not on request. What you can influence is live retrieval, which most assistants run before answering anything specific, and the density and consistency of what the open web says about you. Those two also shape the next model, which is the only realistic route into training data.
What does an LLM SEO service actually deliver?
Honest deliverables are: technical work so pages are retrievable, content restructured so passages can be quoted, entity data made consistent across the site and third-party profiles, off-site work on the comparison pages and directories that models read, and measurement of citation share over a fixed set of buyer questions against a baseline. Guaranteed inclusion in a given model's answer is not deliverable, because the same prompt returns different answers on different days.
How do you measure LLM SEO?
Define twenty to fifty questions a buyer would ask an assistant on the way to choosing a supplier like you, run them against the relevant engines on a schedule, and record who is named, in what order, and which sources are linked. Track that as a rate over time next to brand search volume and direct traffic. A single reading is noise; a monthly series is a signal.
