What Is LLMO? How to Get Cited by AI Search (8 Tactics)

LLMO — what it means

LLMO is optimization for getting your own information cited or referenced when generative AI such as ChatGPT or Google AI Overviews composes an answer.

Hi, I’m Masato — a Webflow specialist who also keeps a constant eye on the leading edge of AI. I run a web production company called Webharu in Shiogama, Miyagi, Japan.

The words “LLMO,” “AIO,” “GEO” and “AEO” have started showing up everywhere lately. If someone asks ChatGPT about something, will your company’s information be introduced inside the answer?
Will your information be picked up by the “AI Overviews” box that sits at the very top of Google search results?

Now that the entry point to search has widened from Google alone to conversations with generative AI, this is not a topic anyone can ignore. The companies most likely to put off adapting are the ones that never relied on signage or portal sites and instead brought in customers through their own publishing. We are one of those companies — we have acquired every client through SEO and AIO with zero ad spend — so this is not somebody else’s problem. In this article I will first sort out the proliferating terminology, then get down to tactics you can start on tomorrow.

Let me start with the conclusion.

LLMO is the practice of optimizing so that generative AI such as ChatGPT cites or references your information when it composes an answer. It does not replace SEO; it sits on the same line, extending good SEO and good site structure. There is no secret trick. What it comes down to is a quiet accumulation of basics: write the conclusion first, define terms in a single sentence, and structure the page. When we redesigned our own site we made structured data and llms.txt part of the standard configuration, and this article itself is written along those lines.

Sorting the terms | what is different about LLMO, AIO, GEO and AEO

Honestly, there is still no industry-standard definition shared across these four terms. It took time for the word SEO to settle too, and the situation now is closer to a new market in which several players have each brought their own label. On the assumption that the scope shifts depending on who is speaking, here are the broad tendencies.

Term	What it mainly covers	Notes
LLMO (Large Language Model Optimization)	Optimization for being cited in answers from LLM-based systems such as ChatGPT, Claude, Perplexity and AI Overviews	The label most widely used in Japan. This article takes it as its axis
AIO (AI Optimization)	Optimization of traffic and citations from AI in general — not only search, but chatbots and recommendation AI as well	Well known in Japan, and often used in a somewhat broader sense
GEO (Generative Engine Optimization)	Winning citations in generative AI search engines	The mainstream term overseas. In practice it is mostly used as a synonym for LLMO
AEO (Answer Engine Optimization)	Direct placement in answer slots such as featured snippets and voice assistants	Sometimes discussed as a separate role from GEO, though the tactics overlap heavily

Overseas the two are sometimes split by role: GEO means being cited inside the text of a generative AI answer, while AEO means appearing directly in the answer slot of a search result. Either way, the foundation is the same pair of conditions — information that is structured and easy to understand, and a source that can be trusted. Part of the reason the labels keep multiplying, I suspect, is that consultants and tools specializing in this field are increasing, and each wants to explain the market in its own words. Our own glossary groups “LLMO / GEO” together as generative AI optimization (see the glossary here).

From the searcher’s side, very few people consciously decide between “I will look this up in ChatGPT” and “I will look this up in Google.” They simply pick whichever window answers fastest, each time. Rather than the people building sites getting hung up on labels, it is enough to understand that the goal is common to all of them: to be chosen by AI as a source. For the rest of this article I use LLMO as the umbrella term.

Why LLMO is being talked about now

The background is simple: the act of searching itself is changing. ChatGPT gained a search function, AI Overviews became a standard part of Google’s results, and dedicated generative AI search services such as Perplexity have taken hold. On top of the usual AI Overviews, Google is also rolling out AI Mode for more conversational, deeper exploration, and the shift from looking at a list of links to getting an answer while talking to an AI is expected to keep strengthening. In Japan too, the number of people using ChatGPT and similar tools as their entry point for looking something up is said to be climbing steadily.

If the entry point that leads to an inquiry or a brochure request is shifting from a list of search results to a single exchange with an AI, then not appearing there translates directly into lost opportunity for a small business. It is a change on the same scale as the end of the era when putting up a sign was enough to bring customers through the door — thinking of it that way makes it easier to picture.

Within that shift, SEO — which used to have exactly one goal, ranking first in search results — has gained a new goal: appearing inside the AI’s answer. If you understand LLMO as the label coined for that new goal, its place in the picture is easy to grasp.

How AI chooses the sources it cites

Let me be upfront here. None of these companies publish their concrete ranking criteria or algorithms. What follows is a summary of the publicly documented mechanisms and of what is generally said, from observation, to be a tendency. It is not an assertion of fact.

Most generative AI search integrations are described as being built on a rough flow of finding candidate pages with a search engine, fetching the content, then summarizing it into an answer. Google’s AI Overviews generates its summary with AI on top of Google’s own search index; ChatGPT search and Bing Copilot are generally understood to draw mainly on Bing-based indexes; and Perplexity is understood to gather information through several search and crawl routes. In other words, being picked up by a search engine in the first place is, in many cases, the precondition for being picked up by generative AI. Incidentally, some generative AI, such as Claude, show the links they referenced inside the answer, which makes it easier to confirm whether your own page was actually used as a source.

Another point is that the speed at which new information is reflected is thought to differ between systems like Perplexity, which fetch pages in real time on every query, and systems that answer on the basis of information gathered in advance.

One more thing worth knowing is that generative AI usually does not cite a whole page: it cuts out just one part of the page and uses that in the answer. Even if the argument of an article as a whole is good, a supporting sentence buried far from the point it supports is hard to pick up. Writing with the question “would this paragraph still make sense if it were lifted out on its own?” in mind is what works in practice. The image to hold is that a single sentence such as “LLMO is optimization for getting cited by generative AI” should carry its meaning to a reader even when extracted alone.

With that said, the characteristics commonly listed for pages that get quoted are as follows.

• It has a clear one-sentence definition: a sentence that can be stated flatly as “X is Y” is easy to use in a summary
• The conclusion comes first: pages with a long preamble and the conclusion at the back tend to drop out of the summary partway through
• It is structured: information organized with headings, bullet points and tables
• It contains primary information: measured data, first-hand experience — information that is not a copy of another page
• It is credible: author and operator information are clear, and the track record can be verified

This overlaps with E-E-A-T (experience, expertise, authoritativeness, trustworthiness), the way of thinking Google has long emphasized in evaluating search rankings. It is easiest to see LLMO as taking what has always been considered good in SEO and pushing it further. People sometimes say that recently updated articles are more likely to be referenced, but updating for the sake of a fresh date — thinning the content in the process — is, I believe, counterproductive.

Eight concrete tactics

From here on, these are tactics you can actually put your hands to. They are ordered starting with the ones you can begin without any special tool. Precisely because measurement is still difficult in this field, I have prioritized what can be done without spending money.

1. Put the conclusion in one sentence directly after a question-style heading

When you write a heading in question form — “What is X?”, “What does X cost?” — state the conclusion flatly in the sentence right after it. Generative AI is thought to find it easier to use pages where the question and the answer sit close together than to dig an answer out from behind a long preamble. The conclusion-first pattern — the conclusion in the body, with reasons and detail following — is, as it stands, the basic form of LLMO. Rather than opening the section after a heading like “What is LLMO?” with background, state the definition flatly in the first sentence and then continue into background and examples. This article follows that discipline of placing a conclusion directly after every heading.

2. Prepare a one-sentence definition for each term

For technical terms and for the names of your own services, prepare a one-sentence definition in the form “X is Y,” of roughly 60 to 120 characters in Japanese (about 20 to 40 words in English). The point is to place it as an independent sentence rather than burying it inside a longer explanation. This article carries one such definition, separate from the body text, as structured data for generative AI to quote.

3. Prepare an FAQ and mark it up with structured data

Prepare the questions your readers are likely to have, with their answers, in FAQ form, and mark them up with FAQPage structured data (JSON-LD). Because the question and the answer become machine-readable as a set, they are easier to pick up not only for generative AI but also for the answer slots in search results. The FAQ at the end of this article is prepared on the same principle.

4. Summarize key points in bullets and tables

Rather than carrying an explanation entirely in prose, organizing the key points into bullet points or a table makes it easier for generative AI to extract the information. A pattern that works well is to close a section that contained a long explanation by restating the key points once as three to five bullets. The summary box at the top of this article, under “In this article,” is one example.

5. Install llms.txt

llms.txt is a text file that tells AI crawlers about the structure of your site and its main pages. It is still a developing mechanism and not every AI necessarily reads it, but the cost of installing it is small, and we install it on our own site as standard. Most production companies do not support it by default yet, so there is nothing to lose by getting it in place now.

6. Make author information and E-E-A-T explicit

Make it clear who wrote the article, who operates the site, and what the track record is. Internal links to a profile page or a company page, and publishing real names and real results, all count. The credibility of a source is generally thought to be a factor for generative AI as much as for search engines, and an article whose author’s track record can be verified stands on stronger ground as a basis for citation than an anonymous piece with thin support. In our case, putting the author’s profile and a link to our results on this very article is part of the same practice.

7. Work on Bing in parallel

Because ChatGPT search is generally understood to use Bing-based search indexes, it is worth registering your site with Bing Webmaster Tools as well as Google and checking its index status there. Think of it as adding Bing as an entry point to an SEO operation that used to be all Google. The setup itself takes less than ten minutes, so there is little reason to put it off.

8. Hold primary information

Holding primary information that no copy of another site can produce — measured data, your own failures, concrete cases — is the strongest tactic over the long run. When something has to be cited from among several articles with similar content, the one with original numbers or first-hand experience is more likely to be chosen. That is exactly why we publish measured figures and lived experience on this blog.

One warning belongs here. Approaches are appearing that call themselves LLMO measures while having AI mass-produce thin articles. I think that has the priorities backwards. What generative AI evaluates is surely not only the visible structure but also whether what is written holds up. Keep the two apart: structuring a page and thinning its content are entirely different things.

Before any misunderstanding: nobody in this field has the answer yet

It is odd to say this after listing eight tactics, but let me be honest. Nobody yet knows what the right answer is.

Placing plenty of Q&A is said to be effective, and we do practice that. Putting a summary at the top of an article, building a solid SEO foundation, writing structured data — those are the sorts of things generally listed. But nobody has yet reached the point of proving that they are the answer. The tactics introduced in this article are what is currently said to work; they are not a settled correct answer.

That is exactly why there is something to watch out for. I think you should be skeptical, at least initially, of any company selling on the flat claim that it can do LLMO for you. I have not been pitched by one yet, but I do see the ads occasionally. And strangely enough, asking an AI about that company’s name often returns nothing. A company selling methods for getting a site cited in AI search is not itself cited by AI.

In a situation where nobody anywhere in the world should yet be the authority in this field, selling on nothing but “we can do it” is, from the buyer’s side, fair grounds for caution. The way to judge is simple: ask ChatGPT about that company’s name. If nothing comes back, then at the very least they have not achieved it for themselves.

Three easy misunderstandings about LLMO

Having laid out the tactics, let me also head off the points that are easy to misread. These are the parts I often see bent conveniently inside sales and consulting pitches.

Misunderstanding 1: stop doing SEO and just do LLMO

In reality it is the opposite. Because most generative AI runs on top of a search engine’s foundation, a site without the SEO basics in place gets limited results from LLMO alone. You will see the line that SEO is old news, but articles with high search rankings are generally thought to appear more often in generative AI answers as well. Without ranking as the denominator, the odds of being picked up by AI do not rise in the first place.

Misunderstanding 2: add structured data and you get cited automatically

Structured data helps AI understand your content, but it does not on its own guarantee citation. It is something like a map: handing someone a map does not bring them to a place that has nothing in it. If the content is thin, being understood does not mean being chosen.

Misunderstanding 3: you do it once and you are done

Both the mechanisms of generative AI and each company’s search algorithms keep changing. Just as search engines have run through algorithm update after algorithm update, this is not something you publish and forget. Go into it expecting to review it regularly.

How to measure the effect

To be honest, measuring the effect of LLMO is not established the way SEO measurement is. The following three are what is realistically possible right now.

Look at AI-referred traffic in GA4

In the GA4 source / medium report, filter for traffic from chatgpt.com, perplexity.ai, bing.com and the like. Because it can appear mixed in with Organic Search or Direct in the session default channel group, you need to add source / medium as a secondary dimension and check them individually. Some of it gets classified as traffic that arrived without following a link, so looking only at the channel breakdown means missing cases.

There are limits to what Search Console shows

Search Console cannot necessarily separate out impressions that came via Google’s AI Overviews as an independent metric at this point. Even when your search performance numbers are rising, it is hard to fully separate whether that came through AI Overviews or through ordinary search results. Alongside that, watching whether branded queries — your company name, your product names — are increasing serves as an indirect clue. It is entirely plausible for someone to learn a company name inside an AI answer and then come to search for it afterwards to check.

Ask ChatGPT and Perplexity directly

The easiest approach is to ask ChatGPT and Perplexity directly about your product names, your service names and the keywords you are targeting. You cannot turn it into a number, but you can confirm by hand whether your own site shows up as a source. A way of thinking is emerging that treats the mere appearance of your name or information inside an answer, even without a click, as a citation — a metric separate from click counts. What I recommend is to fix three questions related to your business (for example, recommended providers in your area and industry, reviews of your service name, and the company name on its own), send the same wording to ChatGPT and Perplexity every month, and note in a table whether your company name or site appeared in the answer. You cannot quantify it, but you can follow the direction of travel. That very difficulty of measurement is, I think, worth understanding as where LLMO currently stands.

The relationship with SEO | an extension, not an opposition

LLMO does not replace SEO. Since most generative AI is built on a search engine’s index, appearing at the top of search results through SEO means the same thing as increasing the pool from which AI can pick you up. Skipping SEO and doing only LLMO is not a realistic idea.

On the other hand, as AI returns answers directly inside the search results, the industry increasingly points out that clicks through to sites fall even when rankings do not change — the so-called zero-click shift. That is exactly why it has become necessary to think not only about ranking but about whether you are cited and your name remains, which is the background to LLMO being talked about at all.

There are also SEO practices that lose value in the AI era. Text stuffed unnaturally with keywords, and articles that dilute the conclusion to pad the word count, suit neither a human reader nor a generative AI summary. Answering the reader’s question in one sentence, showing the grounds, and organizing it into a structure — pushing through with exactly the kind of writing SEO has always rewarded is itself LLMO. Something I feel in practice is that article content you keep updating makes this accumulation easier than the fixed pages of a corporate site do. Service pages in particular, where a sales pitch is the premise, tend not to be treated as a neutral source from a generative AI’s point of view. That is where the value lies in building up LLMO through article content, which finds it easier to stay neutral.

What we do in practice | the implementation on our own site

We really have had inquiries that started with “I looked you up on ChatGPT”

Let me talk about results. We have actually received an inquiry from someone who said they found us by asking ChatGPT. When I asked, they had phrased the question along the lines of “who is the best Webflow company in Japan?” People are consulting AI about which company to hire, and real inquiries are coming out of it — that much I can say, from experience, is definitely happening.

But to be honest, we cannot systematically measure which queries we get cited for, or how often. I hear that our name comes up for phrasings such as “Webflow development company Japan,” but we are not at the stage of tracking that ourselves and turning it into numbers. That is a task still ahead of us. Right now we are stacking up tactics with the aim of reaching the same cited state in the AI website production field too.

There are things we have not done

There are things said to work that we have not yet touched. For example, there is the idea that building up genuine exchanges on question-and-answer venues such as Quora or Reddit makes you more likely to be cited, because AI finds conversations in those places easy to reference. Whether it truly works, though, I cannot judge yet myself, and the reality is that we have not had the capacity for it.

The other thing people say is that shorter, newer writing is more likely to be cited. AI does appear to prioritize newer articles over older ones.

The implementation on our own site

When we fully redesigned our own site with AI (Claude Code) in July 2026, we built structured data and llms.txt in from the start as part of the standard configuration. The full story is in our record of the seven-day full AI redesign. Concretely it is an accumulation of unglamorous work: organizing the hierarchy of heading tags along the actual units of meaning, keeping the meta description and the gist of the body from drifting apart, and making breadcrumbs and related articles explicit through structured data. It feels less like using some special AI tool and more like pushing through with the long-standing practice of clear markup. The JSON structure of this article itself is designed to hold its one-sentence definition and its FAQ in machine-readable form, so the article is meant to be a working example of LLMO.

To be honest, we are not yet at the stage where we can proudly show numbers for how much AI-referred traffic has increased, because the measurement mechanisms are still developing across the industry. Even so, installing structured data and llms.txt costs little, and my frank view is that I cannot find a reason not to do it.

In the end, only the articles I would want to read matter

Having talked at length about tactics, let me close with what I think matters most.

Our editorial policy is to write short and compact, and raise the update frequency. On volume, one article a day, five at the very most, feels about right. As noted above, AI appears to prioritize newer and more concise writing, so we are simply going along with that.

Producing volume is not the goal in itself, though. As E-E-A-T says, being readable is a given — and beyond that we place a great deal of weight on the writing being lived experience, an actual voice. An article that was merely generated by AI has, I think, almost no value.

The reason is simple. I would not want to read it.

There is no point rambling out an article no human wants to read, and no point lining up hard-to-read prose at length. Push the pursuit of getting cited by AI far enough and you end up back at writing an article a person is glad to have read. The word LLMO is new; what you have to do is not that new — that is my honest conclusion for now.

This article, incidentally, was not simply written by an AI. It is composed from what I actually said, including the inquiries we really received, the things we have not got to yet, and the things we cannot measure. Otherwise this article would be its own counterexample.

Our AI Website Production comes with SEO, AIO (AI search optimization), high-speed loading and full-stack construction as standard, and structured data and llms.txt are built in at no extra charge. The details are on our AI Website Production page. If you want to keep adding articles written with LLMO in mind, we also offer an article production service in which our founder edits personally to satisfy SEO and LLMO at once.

If you want to check whether your site is in a state to be cited by AI, start with a free consultation. It is fine to come in without knowing the terminology — we can start by looking at where you are now and sorting out what to tackle first.

FAQ

What is the difference between SEO and LLMO?

LLMO does not replace SEO; it extends it. If SEO is the craft of raising your position in search engine results, LLMO is the craft of being cited or referenced inside a generative AI answer. Because most generative AI is built on a search engine index, a site that does well in SEO has the advantage in LLMO too.

Where should we start with LLMO?

Starting with the things that are free is enough. The three highest-priority tactics are: write the conclusion in one sentence directly after each heading in your existing articles; prepare a one-sentence definition for technical terms and service names; and organize an FAQ and mark it up with structured data. None of them normally costs anything extra.

How should we measure the effect of LLMO?

One approach is to check GA4 referral sources for traffic from chatgpt.com, perplexity.ai and the like. Separating out impressions that came via AI Overviews in Search Console, however, is difficult as things stand. Asking ChatGPT and Perplexity directly about your product or service names, by hand, is also effective.

Is LLMO worth it for a small company’s site?

It is. If anything, a small company that cannot compete on sheer volume of information is better placed to be disciplined about one-sentence definitions and conclusion-first writing. Preparing a clear answer to the question gives a better return than competing on volume.