LLM SEO: What It Means and How to Measure It

LLM SEO is the practice of getting a brand named and cited in answers from large language model products such as ChatGPT, Claude, Gemini and Perplexity. A model knows about you in two ways, from its training data and from pages it retrieves while it answers, and each needs different work and changes on a different clock. You measure the result by sampling many answers, not by checking a rank.

“LLM SEO”, “LLM optimization” and “generative engine optimization” are three names for one discipline. For the general definition, see what GEO is and what AEO is. We compare it with classic search in GEO vs SEO. This post focuses on the model side: how a model forms a view of your brand, why the models disagree, and how LLM visibility tracking works.

Two ways a model knows your brand

1. Training data

A large language model learns from a large snapshot of text collected up to a cutoff date. If your brand appeared often and consistently in that text, the model has a prior about you: what you are, who you compete with, what you are good at. It can name you with no search at all.

You cannot edit that prior directly. You influence it only by what the open web says about you before the next snapshot: reviews, comparison articles, community discussion, documentation, press, directory listings. Then you wait for a new model to be trained and shipped. The provider does not tell you what went into it.

Clock: months. A change you make today can only enter a model’s training data at its next training run, and you rarely know when that is.

Work: broad, consistent presence over time. Describe your brand in the same plain sentence everywhere. Earn coverage on sites that are widely read and widely linked.

2. Live retrieval

Many LLM products search the web before they answer. They send one or more queries to a search index, read a handful of pages, and write the answer from those pages plus the model’s prior. They often cite the pages they used. Perplexity does this on almost every question. ChatGPT, Gemini, Copilot and Google AI Overviews do it on many questions, especially ones about current products and prices.

Clock: days to weeks. A new page that gets indexed and fits the engine’s search queries can start to appear in citations soon after.

Work: be findable and citable. That means indexed pages that answer buyer questions in the first paragraph, honest comparison pages, and presence on the third-party pages the engines retrieve for your prompts. Where an engine exposes the search queries it ran, read them. They show the words the engine used to find sources.

Why the split matters

The two paths need different work and show results at different speeds. If you only watch retrieval, a model update that changes the prior will surprise you. If you only work on the prior, you wait months for feedback. A good LLM SEO program does both, and it tracks citations separately from mentions so it can tell which path moved.

Why the same brand scores differently per model

It is common for one brand to lead on one engine and be absent on another. The reasons are structural:

Different training data and cutoffs. Each provider trains on its own snapshot, at its own date. A brand that grew after one model’s cutoff is new to that model and old news to another.
Different search sources. Engines that retrieve use different search indexes and different query strategies. Google’s products draw on Google’s index. Others use their own or a partner’s.
Different choices about when to search. One engine searches for nearly every product question. Another answers from its prior unless the question looks time-sensitive. The same prompt can be a retrieval answer on one engine and a memory answer on another.
Different answer styles. Some engines list eight brands with a sentence each. Others name two or three. A brand in fifth place is visible in the first style and absent in the second.
Product layers. The answer in the consumer app can differ from the answer the raw API returns, because the app adds its own instructions, search and personal context. Measure the surface your buyers use where you can.

So an “overall” number hides most of the useful information. Read every metric per engine first.

What LLM visibility metrics mean

LLM visibility is the measurement half of LLM SEO. The standard metrics, each defined in our glossary:

Visibility rate: the share of sampled answers that name your brand at all.
Share of voice: answers that name you, divided by answers that name any tracked brand. Some teams call the model version share of model.
Average rank: your mean position among the tracked brands named, when you are named.
First-mention rate: the share of answers where you are the first brand named.
Citation share: how often your domain appears among the sources the answers cite.

Keep “named” and “cited” apart. Named means your brand is in the answer text. Cited means your domain is in the sources. A model can name you from its prior without citing you, and it can cite your page without naming your brand. LLM share of voice tracking in CitePrism counts the two separately, per engine and overall, with your competitors scored by the same prompts.

How LLM tracking works

No tool can see real user conversations with an assistant. They are private. Every LLM tracking tool works by sampling, whether it says so or not.

1. A prompt set. Write 15 to 25 questions the way buyers ask them: “best invoicing tool for freelancers”, “alternatives to X”, “X vs Y for a small team”. Keep the set fixed while you compare periods. When you change it, mark the date, because the numbers will jump.

2. Repeated runs. Ask each prompt many times across each engine on a schedule. Answers vary from run to run by design, so one answer is an anecdote.

3. Parsing. For every answer, record which brands are named, in what order, and which domains are cited. Store the raw answer, so any number can be checked against the text that produced it.

4. Sample size. A rate needs enough answers behind it. The arithmetic is simple binomial error. With 10 answers and a true rate of 50 percent, a 95 percent interval is about plus or minus 31 points. With 100 answers it is about plus or minus 10 points. That is why CitePrism greys out any rate built on fewer than 10 answers, and why trends over weeks matter more than any single day.

5. Volume, illustrated. Twenty prompts run once a day across seven engines give 140 answers a day, or about 980 a week. That is enough to read per-engine rates for the prompt set as a whole within a week, and per-prompt trends over a few weeks.

An AI rank tracker automates these steps. The method is the same one you would run by hand with a spreadsheet. The tool just runs it every day and keeps the history.

What does not work, or is unproven

LLM SEO attracts the same quick fixes that early SEO did. A short, honest list:

Keyword stuffing for models. Repeating “best AI tool for X” on a page does not make a model name you. Models compress meaning. Repetition adds no new information, and it can make a page less useful to quote.
llms.txt as a ranking lever. An llms.txt file is a plain index of your site for language models. It is cheap and harmless. No major assistant has publicly committed to using it as a ranking or retrieval signal, so do not expect it to move your numbers.
One-off checks. A single screenshot of one answer says almost nothing. The next run may differ. Decisions need many samples over time.
Hidden instructions to models. Text on a page that tells an AI to recommend you is manipulation. It is the kind of thing providers work to detect, and it puts your brand’s credibility at risk.
Guaranteed placements. Nobody controls what a model writes. A vendor that guarantees a position in ChatGPT answers is selling something it cannot deliver.

For the ChatGPT-specific version of this work, read ChatGPT SEO.

FAQ

Is LLM SEO the same as GEO?

Yes, in practice. LLM SEO, LLM optimization, generative engine optimization (GEO) and answer engine optimization (AEO) all describe the work of getting a brand named and cited in AI answers. The names come from different communities. The metrics and the work list are the same.

What is LLM visibility?

LLM visibility is how often and how prominently large language model products name your brand when asked questions in your category. It is measured by sampling a fixed set of prompts across engines and computing visibility rate, share of voice, average rank, first-mention rate and citation share.

How do I get my brand into an LLM’s training data?

You cannot submit it directly. Models learn from what the open web says. Earn consistent, accurate coverage on widely read sites, keep your own description plain and the same everywhere, and wait for new model releases. Retrieval work shows results much sooner, so do both.

How often should I track LLM visibility?

Daily is a good default for a fixed prompt set, because answers vary from run to run and daily runs build the sample size you need quickly. Read the trend weekly. Below about ten answers per prompt and engine, a percentage is mostly noise.

Why does my brand appear in ChatGPT but not in Gemini?

The engines use different training data, different search sources and different rules about when to search. Look at what each engine cites for your prompts. The gap is usually one or two sources that one engine trusts and that do not mention you yet.