LLM optimization (LLMO): what it is and how to measure it

LLM optimization (LLMO), also called LLM SEO, is the effort to make your brand familiar to large language models, so that ChatGPT, Gemini, Perplexity and similar assistants recommend it, and describe it correctly, when buyers ask about your market. It works on both sources of an AI answer: what a model learned before release, and the web pages an assistant reads before it replies. Results are measured in the answers themselves, question by question and engine by engine.

The label itself is fading. In the US, “llm optimization” and “llm seo” each get about 720 searches a month and “llmo” about 480, all down 41% to 56% from a year earlier (Google Ads estimates, October 2026), while searches for “what is aeo” rose 179%. The practice is the one our guide to answer engine optimization lays out. What the LLM angle adds is mostly about training data and time lags, and both change how you read your results.

Those results live inside the answers, so you need something that reads answers. Hypervisible, the software we publish, tracks what up to five engines say about your brand and how they rank it (ChatGPT, Gemini, Perplexity, and Google's AI Overviews and AI Mode), along with the sites they cite.

What LLMO means

LLMO means optimizing for large language models: the GPT models behind ChatGPT, Google's Gemini models and the rest. In marketing it is one more name for getting a brand named in AI answers. Wikipedia's article on generative engine optimization lists large language model optimization among that practice's names, along with AEO and AIO.

The term has two other lives, so check which one a vendor means. Engineers say “LLM optimization” when they make a model run faster or cheaper; “llm inference optimization” alone gets about 110 searches a month in the US, by Google Ads' October 2026 estimate. “LLM SEO” sometimes means the opposite of what this article covers. When we checked Google's US results for “llm seo” on October 22, 2026, the top result was a 2024 Data Science Dojo post on “LLM-powered SEO,” which is about using language models to do SEO work.

For marketers, the model view earns its keep by splitting every AI answer into two layers.

Where an AI answer about your brand comes from, based on OpenAI and Google documentation read on October 22, 2026.
	Learned in training	Read at question time
Source	Public web pages, licensed data, and material from users and trainers, up to a cutoff date	Pages returned by a live search: Google's index for Gemini and Google's AI features, search partners for ChatGPT
When it changes	Mostly when a new model ships	Once a page is crawled and comes up in the search
Crawler controls	GPTBot for OpenAI training; Google-Extended for Gemini training and grounding	OAI-SearchBot, PerplexityBot, Googlebot
What you can work on	How often, and how consistently, public pages describe you	Your own pages, and the third-party pages engines retrieve
How it shows	Usually answers with no sources	Answers that cite sources

On training, OpenAI is fairly plain. Its models learn from information publicly available on the internet, from data accessed through partners, and from what users, human trainers and researchers provide. During training, a model picks up how words typically appear together; it keeps no copy of the pages, only adjusted parameters. Our reading for brands: a company that public pages often mention alongside its category and city is more likely to come up when a model answers from memory.

Training knowledge also has an end date. OpenAI's model page lists a knowledge cutoff of April 30, 2026 for GPT-6 Astra, its flagship API model in October 2026. A product you launched in June 2026 doesn't exist for that model unless the assistant runs a search.

LLMO, GEO, AEO: the same job?

Close enough that you shouldn't staff them as separate projects. AEO stresses the answer, GEO the generated text and its sources, LLMO the model and what it knows; the full side-by-side, with SEO, is in AEO vs GEO vs SEO.

Google's position covers most of the ground. In the July 10, 2026 version of its guide for site owners on generative AI in Search, Google mentions AEO and GEO by name, never LLMO, and says that, from Google Search's perspective, optimizing for generative AI search “is optimizing for the search experience, and thus still SEO.” Outside AEO or GEO advice, it adds, deserves the same scrutiny as any other SEO advice. By that logic, an LLMO plan aimed at AI Overviews and AI Mode is an SEO plan with a new label.

A standard SEO checklist misses two things. OpenAI and Perplexity send their own bots and pull from their own search sources, so being indexed by Google leaves part of the job undone. And model training follows the vendor's release calendar: whatever you publish today may shape a model trained next year, and nobody outside the lab can tell you when. That training layer is the one piece LLMO can claim as its own. We'd still use the word only with clients who already use it.

What LLM optimization work involves

LLMO work is SEO and reputation work done with one extra question in mind: what would a model say about you, and from which pages. We split it into five jobs.

Decide what each crawler may do

OpenAI uses OAI-SearchBot for ChatGPT search and says sites that block it won't appear in ChatGPT search answers. GPTBot collects content for training, and the two settings are independent. Perplexity asks sites to allow PerplexityBot. Google-Extended controls whether Google may use your content for Gemini training and grounding, with no effect on Google Search; AI Overviews and AI Mode follow the usual Googlebot and snippet rules.

Make the training decision on purpose. If you want the next generation of models to know your company, blocking GPTBot and Google-Extended works against that goal. A publisher with paywalled archives may reasonably decide otherwise. Then check that your firewall or CDN doesn't turn these bots away on its own: OpenAI publishes the IP addresses its crawlers use and asks sites to allow requests from them.

Publish the facts you want repeated

A model can only repeat what it found. Put your plain facts on one page that's easy to read: what you sell, to whom, where, since when, and prices if you publish them. A commercial cleaning company in Atlanta might state that it cleans offices and medical clinics across metro Atlanta, works nights, and has done so since 2011. Use the same wording on your Google Business Profile, LinkedIn page and the directories of your trade. Schema markup is optional for Google's AI features, according to Google, so treat it as a help for regular results and keep the facts in the visible text.

Write what only you can write

Google's July 2026 guide draws the line with two titles. “7 Tips for First-Time Homebuyers” is what it calls commodity content, built on common knowledge. “Why We Waived the Inspection & Saved Money: A Look Inside the Sewer Line” is non-commodity: first-hand experience nobody else could have written. The guide adds that its AI systems look at a variety of sources, so a distinct point of view helps a page stand out. Your own numbers, test results, prices and case notes fit that description.

Get covered where models read

A question like “best payroll software for a 30-person dental group” is often answered from pages you don't own. Find out which sites the engines cite for your questions, then earn a place on them: real customer reviews, data a reporter can use, an accurate profile on the software or service directories of your field. Our article on how ChatGPT chooses its sources shows where those citations tend to come from.

Fix wrong facts where they start

When an assistant gets you wrong (old hours, a discontinued product, last year's price), open the sources it cites and correct the page that carries the error, whether it's yours or a directory's. Ask again after the next crawl. An error in an answer with no sources most likely comes from training, and only a later model will drop it, which is one more reason to fix the public pages now.

How to measure LLMO results

Put a fixed set of questions to each assistant on a schedule and record what comes back: your brand's presence, its place in the answer, the competitors beside it, the sources cited and the accuracy of the facts. For LLMO, add one split that GEO reports often skip: answers given from memory versus answers built on a search.

That split tells you how fast a fix can land. A wrong fact that comes with a cited source can be corrected this month. A wrong fact in an answer with no sources waits for the next model. OpenAI's help pages say answers that use web search can include citations, so the presence of links is the practical marker.

What to track for LLMO, and where each number comes from, as of October 2026.
What to track	What it tells you	Where it comes from
Citation rate	How often each engine names you	Fixed questions, asked on a schedule
Position in the answer	Named first, or as an afterthought	The same answers
Share of voice	Your mentions against your competitors'	The same answers
Cited sources	Which pages the engines rely on in your category	Links in answers that used search
Factual accuracy	Whether what's said about you is right and current	Answers to questions that name you
Memory or search	How quickly a correction can show up	Whether the answer cites sources
Impressions in Google's AI features	How often Google shows your pages in AI Overviews and AI Mode	Search Console's Generative AI performance report

Two habits keep the numbers honest. Freeze the question list, because a reworded question gets a different answer, and read every engine separately. OpenAI says there is some built-in randomness in how its models respond. One answer on its own proves little, and a trend over several weeks is worth far more. The method for comparing yourself with competitors is in our guide to AI share of voice, which also covers the “human-AI awareness gap” that a June 2025 Harvard Business Review article proposed: how well people know your brand, set against how often models bring it up.

Measuring LLMO with Hypervisible

Hypervisible is the tool we use and recommend for this tracking, and we should say plainly that we publish it. The French agency that makes it, NEXUS GEO, focuses on GEO alone; the software draws on data from Google and the AI engines.

Of its reports, two matter most for LLMO. The record of how each engine describes your brand, tone included, is where an outdated price or a wrong address shows up first. The list of sites each engine cites tells you which page carries the error, and whether the answer leaned on a search at all. Around those sit your citation rate, your average position and your ranking against the competitors that turn up beside you, on ChatGPT, Perplexity, Gemini, Google AI Overviews and Google AI Mode.

The rhythm suits the slow layer of LLMO. Paid plans put the same list to three of those engines each Monday (ChatGPT, Gemini and Google AI Overviews), or to all five, and show the change since the last reading in points, which is how a trend over several weeks becomes visible (pricing). The method also has a public base: Martin Noale, who founded NEXUS GEO in January 2026, has made public a checklist of 47 criteria for visibility in AI answers, along with about a hundred guides on nexus-geo.fr.

Claude and Copilot aren't covered, and a weekly reading can miss a change that comes and goes within a day. The free AI visibility checker is a quick way in: 10 questions go to three of the five engines (ChatGPT, Gemini and Google AI Overviews), five about your sector that never mention you and five that do, and the 30 replies get read in 5 to 10 minutes. You won't need a credit card, and your results wait in your account.

Common mistakes

Most LLMO mistakes come from treating it as a set of tricks for machines, when it rewards the same things as good SEO and a solid reputation.

Blocking the wrong crawler. A site that blocks OAI-SearchBot to keep its content out of training also drops out of ChatGPT search answers. GPTBot is the one that controls training.
Publishing a page for every prompt. Google's July 2026 guide says that creating separate content for every variation of a search, fan-out queries included, mainly to manipulate rankings or AI responses, violates its scaled content abuse spam policy.
Counting on llms.txt. Google's guide lists llms.txt files and other AI-only markup among the things its Search ignores. Some AI services may read the file. A crawler blocked by your firewall, or a page that never states your prices, matters far more.
Stuffing keywords or brand names. In the 2023 GEO study by researchers from Princeton University and IIT Delhi, keyword stuffing barely helped visibility in generated answers and did worse than no change on Perplexity. The rewrites that helped most added citations, quotations and statistics.
Buying reviews or planted mentions. The Federal Trade Commission announced a rule on August 14, 2024 that bans the sale and purchase of fake reviews, including reviews written by AI, and lets the agency seek civil penalties. As for inauthentic mentions, Google says chasing them “isn't as helpful as it might seem.”
Judging from one answer. One screenshot of ChatGPT naming you, or skipping you, is a single draw. Look at the trend on fixed questions.
Paying for “insider” access. Google says no outside tool can see into its internal ranking or AI systems. What a tool can do is read the answers your buyers get.

Your brand

The free analysis asks 10 questions to three AI engines (ChatGPT, Gemini and Google AI Overviews), reads the 30 answers in 5 to 10 minutes and keeps your dashboard in your account.

Frequently asked questions

What is SEO for LLMs?

SEO for LLMs, also called LLM SEO or LLMO, is the work of getting large language models and the assistants built on them, such as ChatGPT, Gemini and Perplexity, to know your brand, describe it correctly and name it in their answers. It covers the pages assistants read when they search the web and the public pages models learn from in training. The term sometimes also means using LLMs to do SEO work.

Is LLMO the same as GEO?

In practice, yes. GEO, generative engine optimization, comes from a 2023 research paper on AI-generated answers; LLMO frames the same goal around the language models themselves, including what they absorb in training. Both are measured the same way, in the answers. Google's July 2026 guide names AEO and GEO, not LLMO, and says that for Google Search this work is “still SEO.”

How do you optimize for large language models?

Let the AI crawlers you want reach your site, such as OAI-SearchBot and PerplexityBot, and decide separately whether training crawlers like GPTBot may collect your pages. Publish clear, consistent facts about your business, write content based on your own experience and data, and earn honest coverage on the review sites, comparisons and trade press that AI answers cite. Then check the answers on a schedule and correct errors at their source.

How do you measure LLM optimization?

Hypervisible, the software we publish, is how we'd do it: every Monday it puts a frozen list of buyer questions to ChatGPT, Gemini and Google AI Overviews, with Perplexity and Google AI Mode joining them on the five-engine plans, and records your presence, position, competitors, cited links and how you're described. We recommend it because it reads each engine separately on the same questions, which keeps the trend readable. By hand, note the same things; answers with links react quickly to fixes. Search Console adds impressions in Google's AI features.

Is LLM SEO different from regular SEO?

Mostly not. Google's July 2026 guide says that optimizing for its generative AI features is “still SEO,” and AI assistants that search the web depend on pages being crawlable and clear. LLM SEO adds two checks: access for the crawlers of OpenAI and Perplexity, and a decision on whether training crawlers such as GPTBot may use your pages. Measurement also differs, since results show up inside answers rather than in rankings.