LLM SEO
LLM SEO: the playbook, minus the folklore
LLM SEO is the work of making a brand something large language models name, and describe correctly, when people ask them buying questions. There is no ranking to climb: the answer is regenerated per question, per model, per day. What you can change are its inputs, which is what this playbook is for.
People arrive at this topic asking how to rank in ChatGPT, and that framing is the first thing to unlearn. Nothing here is a position you hold; it is a description the model reassembles every time. Every step below carries the number, from our own 363 recorded answers, that earns its place on the list.
STEP 1
Measure before you touch anything
Ask the models the questions your buyers type, and keep the transcripts. Not a score: the actual answers, with who was named and what was cited. Everything after this step is spending; this step is the only thing that tells you whether the spending worked.
Observed
Our free check does this across six models and keeps every word. Run it on your domain before reading the rest of this list.
STEP 2
Cover every model, because one model tells you almost nothing
The single most repeated mistake in this field is optimising for ChatGPT and assuming the rest follows.
Observed
Across 62 questions answered by more than one model, the average overlap between two models’ brand lists was 23.3%, and of the ~22.5 brands named per question, under one was named by all models.
STEP 3
Get described where the engines already cite
The answer is assembled from pages you do not own. The work is being accurately described on the roundups, comparisons, reviews and communities the engines already reach for in your category, and your own site is the one source that cannot vouch for you.
Observed
Citations in our corpus spread across 1,952 distinct domains, the top 20 holding just 9%. Breadth beats a short list.
STEP 4
Take review platforms as seriously as Reddit
The folklore says Reddit and Wikipedia are the whole game. Live citations disagree: comparison and review platforms appear more often than either, because they are shaped exactly like the answer the engine is trying to write.
Observed
Share of our answers citing each at least once: review platforms 10%, Reddit 6%, Wikipedia 2%. Wikipedia’s influence is real but lives in the memory layer, not in live retrieval.
STEP 5
Write pages an engine can lift
Answer in the first forty words, make headings questions, put a number or a definition where a quote would go. Extraction happens at passage level, and the passage that gets lifted is the one that stands alone.
Observed
The full version of this is its own guide: writing the page that gets lifted.
STEP 6
Feed the memory layer, not just retrieval
A meaningful share of answers cite nothing at all: the model answers from what it already believes about your category. You do not reach that layer with one page. You reach it by being described consistently, in the words buyers use, across enough of the web for long enough.
Observed
14% of all recorded answers exposed zero sources. For those, whatever the model already believed was the whole answer.
STEP 7
Re-run the same questions, on a schedule
The answers are regenerated continuously, so a single measurement is a snapshot. Keep the question set fixed, re-run it, and compare transcripts. Movement you can see beats vibes from a dashboard, and it is the only honest way to attribute results to the work.
Where LLM SEO budgets actually die
The playbook above is what to do. Just as important, because this market is young and loud, is what quietly eats budgets without moving a single answer:
Buying a dashboard instead of a change
Monitoring tells you the score. It does not play the game. A tracking subscription with no budget left for changing what the models read is a thermometer purchased instead of medicine, and it is the single most common shape of wasted spend we see.
Rewriting your own site for the fifth time
Your site earns you retrievability and correct self-description, and then its returns collapse, because the answer is assembled from other people’s pages. The fifth homepage rewrite competes with the first for the same single slot: witness number zero, the one the engine trusts least about you.
Paying for guaranteed citations
Nobody can sell a position inside a model. There is no ranking, no submission form, no ad unit in the answer. What is honestly purchasable is description: real articles on real sites the engines read. If the invoice says “guaranteed AI citation”, the product is the invoice.
Optimising for last quarter’s folklore
llms.txt files, keyword-stuffed FAQ farms, schema as a silver bullet: each had a season of conference slides, none shows up as a cause in our citations. The habit that survives every season is duller: be described accurately, in many places, and measure.
How to run the measurement yourself
You do not need us for step 1, and this guide is more useful if it shows the whole method. The manual version:
- 1. Write down 10 questions your buyers would actually type, in their words. Pull them from sales calls, support tickets and search data, not from your positioning document.
- 2. Ask each question in a fresh chat, on each assistant your buyers use, with browsing on where the option exists. No fresh chat, no valid reading: history contaminates the answer.
- 3. Record four things per answer: were you named, at what position among the brands, who else was named, and which sources the engine exposed. A spreadsheet is fine. Screenshots age badly; text survives.
- 4. Repeat monthly, same questions, and only compare like with like: the same question on the same engine. Cross-engine averages destroy the only signal you have.
That is exactly what our free check automates, across six engines at once, with the transcripts kept for you. The method is not the secret; running it consistently is.
The sources, category by category
“Get described where the engines already cite” is only useful if you know where that is, and it is different in every category. These are the sites the engines cited most in the categories we have measured deepest, with the checked company’s own domain excluded. This is the table the generic advice cannot print, because it changes per market.
temporary phone numbers SMS verification
51 recorded answers
- textverified.com×18
- burnerapp.com×11
- apps.apple.com×9
- bestforandroid.com×9
- twoline.io×9
- verifynumber.io×9
music dance and arts school lessons in Dubai
51 recorded answers
- melodica.ae×18
- balletcentredubai.com×12
- bayut.com×12
- coursetakers.ae×10
- dubaisbest.com×9
- jamesandalex.com×9
music dance arts school lessons
34 recorded answers
- schoolofrock.com×8
- melodica.ae×7
- sruthimusic.com×5
- balletcentredubai.com×4
- bayut.com×4
- classicdance.ae×4
recipe blog and cookbook
34 recorded answers
- eathealthy365.com×8
- gourmetads.com×8
- allrecipes.com×7
- bloggers.feedspot.com×6
- epicurious.com×6
- reciperadar.com×6
Not on the list for your category? That is what the free check finds: the exact sites the engines quoted when asked about businesses like yours.
The questions with real demand behind them
LLM SEO starts from what people actually ask, so here are the buyer questions with the most measured search volume in our corpus, each linking to what every model answered:
“Which recipe website has the best collection of home-cooked dinner recipes?”
550,000 searches a month behind it · 5 models answered · names Allrecipes most
“Which AI video generator should I use for creating content?”
246,000 searches a month behind it · 6 models answered · names Runway most
“What is the best video editing software for creating professional content?”
201,000 searches a month behind it · 6 models answered · names DaVinci Resolve most
“Which real estate agents should I use to sell my property in London?”
201,000 searches a month behind it · 6 models answered · names Knight Frank most
“What is the best CRM software for managing customer relationships?”
165,000 searches a month behind it · 6 models answered · names Salesforce most
“Which recipe websites have the most reliable dinner recipes?”
165,000 searches a month behind it · 6 models answered · names Serious Eats most
Step 1 takes about a minute
Put your domain in. We pull the questions your buyers actually search, ask six live models, and hand you the transcripts: your name highlighted where it appears, the competitors named where it does not.
Run the free checkFree, no account, no card.
FAQ
Frequently asked questions
What is LLM SEO?▾
LLM SEO is the work of making a brand something large language models name and describe correctly when people ask them buying questions. The name emphasises the thing doing the reading: the model. The same work goes by GEO, AEO and AI SEO.
How do I rank in ChatGPT?▾
You do not, because there is no ranking. An assistant regenerates its answer per question, per model, per day, from what it can retrieve plus what it already believes. What you can do is change those inputs: be described accurately on the pages it reads, across every model rather than one, and measure whether the answers move.
Does LLM SEO replace normal SEO?▾
No. The pages that get you cited still have to be found, crawled and trusted, which is classic SEO. LLM SEO adds a second audience for the same work: systems that read widely and answer once. Most of what earns citations, clear pages with something specific on them, described consistently across the web, is good SEO anyway.
How do I measure LLM visibility?▾
Ask the models your buyers’ questions and record the answers. Averages hide everything here, so keep transcripts: which model, which question, who was named, in what order, citing what. That is exactly what our free check does, across six models, with every answer kept word for word.
How often do the models change their answers?▾
Often enough that a single check is a snapshot, not a verdict. Retrieval-heavy engines shift with the web; memory-heavy ones shift with model updates. Re-running the same questions on a schedule is the only way to tell a trend from an accident.
What content works best for LLM SEO?▾
Content shaped like the answer the model is about to write. Comparisons, direct recommendations for a named situation, and pages carrying an original number all keep appearing in our citations. Brand essays about yourself almost never do, because the model is looking for a witness, not a self-introduction.
Do I need different content per model?▾
No. You need broader description, not model-specific pages. The models disagree because they retrieve and weigh sources differently, and the same accurate description spread across more of the web is what narrows the gap. Writing "for Claude" or "for ChatGPT" is not a real thing, whatever the vendor deck says.
Is LLM SEO worth it for small companies?▾
It is most valuable exactly where you cannot win classic SEO head-on. An assistant answering a specific buying question reaches for whoever is described well for that question, not whoever has the biggest domain, and our transcripts regularly show small companies named next to giants for precisely phrased needs.