Generative AI is changing how people search for information.

Instead of searching Google, opening several websites, and comparing information manually, users can now ask ChatGPT, Google AI Mode, Gemini, Perplexity, or Copilot a question and receive a synthesized answer almost immediately.

This shift is changing the role of search optimization.

Ranking on Google still matters. But businesses increasingly need to consider another question:

Will AI systems understand, trust, cite, and recommend our brand when users ask relevant questions?

This is where LLMO—Large Language Model Optimization—comes in.

LLMO is the practice of improving your website, content, brand signals, and digital presence so that generative AI systems can more easily understand your organization and potentially use or mention your information in AI-generated answers.

In Western digital marketing, you may also encounter closely related terms such as GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), AI SEO, and AI search optimization. Terminology is still evolving, but the underlying objective is similar: becoming visible wherever AI systems influence discovery and purchasing decisions.

This guide explains what LLMO is, how it differs from traditional SEO, why it matters, how query fan-out changes content strategy, and how businesses can build an effective AI-search optimization program.

What Is LLMO?

LLMO stands for Large Language Model Optimization.

It refers to optimizing a company’s digital information so that large language models and AI-powered search products can accurately understand, retrieve, reference, cite, or recommend that company.

Platforms relevant to LLMO include:

• ChatGPT
• Google AI Overviews
• Google AI Mode
• Gemini
• Perplexity
• Microsoft Copilot
• Other AI assistants and search experiences

Traditional SEO primarily focuses on helping pages rank prominently in search results and generate clicks.

LLMO expands the objective.

Instead of asking only:

“Can our page rank for this keyword?”

marketers must increasingly ask:

“When someone asks an AI system this question, does our company appear in the answer?”

That distinction is important.

A consumer researching software, a marketing agency, a financial product, or an enterprise vendor may increasingly form an opinion before visiting any company’s website.

The AI answer itself becomes part of the consideration process.

LLMO Does Not Replace SEO

LLMO should not be viewed as a replacement for SEO.

A better way to think about it is:

SEO provides the foundation. LLMO adds an AI-discovery layer on top of that foundation.

Google explicitly states that established SEO best practices remain relevant to its generative AI search experiences. Pages generally need to be crawlable, indexable, technically accessible, and eligible to appear in Google Search before they can be surfaced through Google’s generative search features.

Strong LLMO therefore usually starts with strong SEO.

Technical accessibility, useful content, clear site architecture, authoritative information, first-party evidence, good user experience, and trustworthy brand signals matter in both disciplines.

The difference is primarily what success looks like.

SEO vs. LLMO: What’s the Difference?

SEO and LLMO share many fundamentals, but their primary objectives and measurement frameworks differ.

The most important point is that SEO and LLMO overlap significantly.

Good AI-search optimization is rarely achieved through a single trick or technical file. It usually comes from improving the overall quality, clarity, authority, and accessibility of your digital presence.

Why LLMO Matters Now

The traditional search journey looked something like this:

Search → Browse results → Visit websites → Compare options → Make a decision

AI-assisted discovery can shorten that process:

Ask AI → Review answer and recommendations → Visit selected sources if necessary → Make a decision

This means brands increasingly compete for visibility before the click.

A company can potentially influence a user’s decision even when the user never visits its website.

Conversely, a business that ranks reasonably well in traditional search may still be absent from an AI-generated comparison or recommendation.

Google itself has been expanding generative search experiences such as AI Overviews and AI Mode as part of this broader change in information discovery.

For marketers, the implication is straightforward:

Search visibility can no longer be measured by blue-link rankings alone.

Understanding Query Fan-Out

One of the most important concepts in AI search is query fan-out.

In traditional search, users often conduct a series of separate searches.

For example, someone researching SEO expansion in Australia might search:

• Australian SEO market
• SEO competition in Sydney
• B2B SEO Australia
• SEO strategy Melbourne
• Australian search behavior

The user performs the research step by step.

With AI-powered search, a complex question can effectively trigger multiple related information-retrieval processes behind the scenes.

An AI system can break a broader question into subtopics, gather information relevant to those subtopics, and then synthesize the findings into one answer.

This creates an important change for content strategy.

You are no longer optimizing only for one keyword.

You are optimizing for an information network surrounding the user’s question.

What Query Fan-Out Means for Content Strategy

Consider a user asking:

“What is the best SEO strategy for a B2B software company entering Australia?”

A useful AI answer might require information about:

• Australian search demand
• B2B buyer behavior
• regional competition
• technical SEO
• localization
• content strategy
• link acquisition
• industry-specific examples
• common mistakes
• expected timelines

A company that publishes only one generic “SEO Australia” landing page may provide insufficient evidence for this broader research process.

A stronger approach is to develop interconnected content covering the major subtopics an AI system—or a human researcher—would need to answer the question properly.

This is one reason topic clusters and strong internal linking become even more valuable in AI search.

The Four Pillars of an LLMO Strategy

At Tokyo SEO Maker, we recommend thinking about LLMO across four broad areas:

1. Strategy
2. On-site optimization
3. Off-site optimization
4. Measurement and continuous improvement

1. Develop an LLMO Strategy

Start by defining the questions for which you want your brand to appear.

Do not begin with technical implementation.

Begin with customer intent.

Identify prompts such as:

• “What are the best enterprise SEO agencies?”
• “Which SEO company specializes in entering the Japanese market?”
• “What should a US company consider before launching SEO in Japan?”
• “Which agencies provide international SEO consulting?”
• “How does SEO in Japan differ from SEO in the United States?”

Then prioritize those prompts according to:

• commercial value
• relevance to your services
• likelihood of AI-assisted research
• competitive difficulty
• your ability to provide unique evidence

This creates a prompt map that plays a similar role to keyword research in traditional SEO.

Next, determine the criteria an AI system—or a user—would reasonably need in order to recommend a company.

For an SEO consultancy, for example, those criteria could include experience, specialization, case studies, geographic expertise, methodology, client results, team credentials, pricing transparency, or independent recognition.

Your job is then to create verifiable evidence for those attributes.

2. Optimize Your Website for AI Understanding

Once the strategy is defined, optimize your own website.

This includes content, technical implementation, site architecture, and trust signals.

Create Clear, Answer-First Content

Generative AI systems need information that can be interpreted without excessive ambiguity.

Useful formats include:

• concise definitions
• direct answers
• FAQs
• numbered processes
• comparison tables
• step-by-step instructions
• clearly labeled statistics
• case studies
• expert commentary

Avoid forcing readers to work through several paragraphs before discovering the answer to a simple question.

A strong structure is often:

Answer → Explanation → Evidence → Example → Next step

This improves usability for both humans and machines.

Build Topic Clusters

Create comprehensive coverage of the subtopics surrounding commercially important questions.

A strong cluster might contain:

Hub page:
International SEO

Spoke pages:
SEO in Japan
Multilingual SEO
International keyword research
International link building
hreflang implementation

Supporting pages:
Case studies
Market-specific statistics
Technical guides
FAQs
Industry-specific examples

Strong internal linking helps establish relationships between these resources.

Publish Original Evidence

One of the most powerful ways to differentiate your content is to publish information competitors cannot simply reproduce.

Examples include:

• proprietary research
• customer surveys
• case-study results
• original datasets
• experiments
• benchmarks
• first-hand industry experience
• expert analysis

If dozens of websites repeat the same generic explanation, an AI system has many possible sources.

If your company publishes the original study, dataset, or case evidence behind a claim, your page becomes a much more distinctive reference.

Strengthen Entity Clarity

Search engines and AI systems need to understand who your company is and what it is known for.

Keep important information consistent across your digital presence, including:

• company name
• products and services
• locations
• founders and executives
• areas of expertise
• credentials
• contact information
• awards
• major clients or case studies where disclosure is permitted

Your About, Team, Service, Case Study, Author, and Contact pages should reinforce the same underlying identity.

Use Structured Data Appropriately

Structured data can help search engines interpret information on a page, including organizations, people, products, articles, breadcrumbs, and other entities.

Use schema markup where appropriate and ensure it accurately reflects visible page content.

However, structured data should not be treated as a magic AI-ranking switch. Google does not require special schema markup solely for inclusion in AI Overviews or AI Mode.

Maintain Strong Technical SEO

AI optimization does not excuse poor technical SEO.

Continue to address:

• crawlability
• indexability
• canonicalization
• internal linking
• page speed
• mobile usability
• JavaScript accessibility
• duplicate content
• URL architecture
• XML sitemaps
• robots directives

If search systems cannot reliably access your content, other optimization efforts have limited value.

3. Strengthen Off-Site Authority

Your own website is only one source of information about your company.

AI systems may encounter your brand across:

• news publications
• trade publications
• professional associations
• customer websites
• review platforms
• comparison sites
• directories
• social networks
• YouTube
• podcasts
• conference websites
• research reports

This makes third-party visibility increasingly important.

Earn Authoritative Mentions and Links

Traditional backlinks remain useful, but LLMO expands the focus from links to mentions and citations more broadly.

A respected industry publication mentioning your company can reinforce its relationship with a specific topic even when a traditional followed backlink is not involved.

The goal should not be mass citation generation.

Focus on credible, contextually relevant third-party sources.

Make Expertise Visible

Expertise should exist beyond your About page.

Encourage subject-matter experts to contribute through:

• industry publications
• webinars
• conference presentations
• interviews
• podcasts
• professional communities
• original research
• video content

When the same expert or organization is repeatedly associated with a subject across credible environments, the entity becomes easier to understand.

Publish Verifiable Case Studies

Case studies are particularly useful because they connect expertise with evidence.

Instead of saying:

“We are experts in international SEO.”

provide evidence such as:

“Following a 12-month international SEO program, non-branded organic traffic in the target market increased by X%, while qualified leads increased by Y%.”

Specific evidence is far more useful to prospective customers—and potentially to AI systems attempting to compare providers—than generic promotional language.

How to Measure LLMO Performance

Traditional SEO metrics remain valuable, but they do not tell the entire AI-search story.

We recommend monitoring three additional categories.

AI Share of Voice

AI Share of Voice measures how frequently your brand appears in AI-generated answers for strategically important questions relative to competitors.

For example, track prompts such as:

• best international SEO agencies
• best Japan SEO companies
• SEO agencies for US companies entering Japan
• international SEO consultants
• multilingual SEO services

Then monitor whether:

• your brand appears
• competitors appear
• your website is cited
• third-party pages mentioning your brand are cited
• the description of your company is accurate
• the recommendation context changes over time

AI Referral Traffic

Measure visits arriving from AI products where referral information is available.

Track:

• sessions
• engaged sessions
• lead submissions
• demo requests
• purchases
• assisted conversions
• conversion rate
• revenue

AI-generated referral volume may still be smaller than traditional organic search for many companies, so avoid judging the channel on traffic volume alone.

Users who click after receiving a detailed AI-generated recommendation may already be relatively far along in their decision process.

Zero-Click Brand Impact

Some AI visibility creates value without generating an immediate website visit.

Potential indicators include changes in:

• branded search volume
• direct traffic
• brand mentions
• sales-call attribution
• survey responses
• “How did you hear about us?” responses
• prompted brand awareness

Zero-click impact is inherently harder to measure precisely, so these metrics should be interpreted directionally rather than treated as perfect attribution.

A Practical LLMO Optimization Process

A simple operating cycle can make LLMO easier to manage.

Step 1: Establish Your Baseline

Test your company, products, services, executives, and important commercial prompts across relevant AI platforms.

Record:

• whether you appear
• how you are described
• which competitors appear
• which websites are cited
• factual inaccuracies
• important topics where you are absent

Step 2: Improve Your First-Party Information

Strengthen the pages AI systems and users can rely on as authoritative company references.

Prioritize:

• service pages
• About pages
• author biographies
• case studies
• research
• FAQ pages
• product documentation
• comparison pages
• methodology pages

Step 3: Build Third-Party Evidence

Increase credible external evidence through:

• digital PR
• partnerships
• industry publications
• customer stories
• professional associations
• expert contributions
• original research

Step 4: Monitor the Same Prompts

AI answers can vary between models and over time.

Maintain a consistent set of commercially important prompts and check them regularly.

Using the same prompt set gives you a more meaningful performance baseline than testing random questions.

Step 5: Improve and Repeat

If information is inaccurate, strengthen the authoritative source.

If competitors appear more frequently, determine what evidence they possess that you do not.

If your brand appears but receives few visits, improve the pages and calls to action users encounter after an AI referral.

LLMO is an ongoing process rather than a one-time implementation.

Tools for Monitoring AI Visibility

AI-search measurement is developing quickly, and several tools now help marketers monitor brand presence across generative platforms.

Ahrefs Brand Radar

Ahrefs offers AI visibility monitoring that can identify brand mentions and citations across platforms including ChatGPT, Gemini, Perplexity, Copilot, and Google AI experiences.

Semrush AI Visibility Toolkit

Semrush’s AI Visibility Toolkit provides features for monitoring brand visibility, competitors, prompts, AI citations, and technical accessibility for AI-search environments.

Other AI-visibility platforms are also emerging rapidly.

Regardless of the tool you choose, automated tracking should be supplemented with manual testing of your highest-value prompts.

No current tool provides a complete picture of every AI-generated answer a potential customer may encounter.

Five Practical LLMO Priorities

If your organization is just getting started, focus on these five areas.

1. Make Your Brand an Unambiguous Entity

Clearly explain:

• who you are
• what you do
• where you operate
• who you serve
• what makes you different

Maintain consistency across first-party and credible third-party sources.

2. Improve E-E-A-T Signals

Demonstrate real-world:

• experience
• expertise
• authority
• trustworthiness

Useful signals include expert authorship, editorial policies, verifiable credentials, case studies, transparent company information, and first-hand experience.

3. Create Original Content

Generic summaries are becoming increasingly easy to produce.

Original research and first-hand experience are much harder to replace.

Invest in content containing information that exists because your organization produced it.

4. Write for Retrieval and Understanding

Make important information easy to locate.

Use:

• descriptive headings
• concise answers
• logical sections
• tables where useful
• FAQs
• definitions
• examples
• descriptive internal links

Do not sacrifice natural writing for machines. The goal is clarity for both humans and automated systems.

5. Keep Doing SEO

Technical SEO, strong content, authority building, and good site architecture remain essential.

Google’s own current guidance emphasizes that optimizing for its generative search experiences is fundamentally an extension of good search optimization rather than an entirely separate discipline.

What About llms.txt?

You may encounter recommendations to add an llms.txt file to your website.

The concept is still experimental and should not be treated as a substitute for SEO fundamentals.

There is currently no indication from Google that an llms.txt file is required to appear in Google AI Overviews or AI Mode.

If you experiment with emerging AI-specific technical standards, treat them as supplemental measures while continuing to prioritize crawlability, indexability, structured site architecture, valuable content, and clear entity information.

The Future of Search Optimization

The fundamental objective of search marketing has not changed.

Businesses still need to become the most useful and trustworthy answer to a customer’s question.

What is changing is where that answer appears.

Previously, success was heavily concentrated on ranking in a list of search results.

Now that answer may appear inside:

• a traditional Google result
• an AI Overview
• Google AI Mode
• ChatGPT
• Perplexity
• Gemini
• Copilot
• another AI assistant that has not yet become mainstream

For that reason, the future of search optimization is unlikely to be a choice between SEO and LLMO.

Successful organizations will need both.

SEO helps your information become discoverable.
LLMO helps your brand become understandable, referenceable, and recommendable in AI-mediated discovery.

The companies that perform well will be those that provide clear information, credible evidence, distinctive expertise, strong technical foundations, and consistent signals across the broader web.

Summary

Generative AI is moving part of the customer journey from websites and search-result pages directly into AI-generated answers.

That does not make SEO obsolete.

It makes strong SEO—and the information architecture behind it—even more important.

LLMO extends traditional search optimization by asking organizations to consider not only whether a page can rank, but whether AI systems can accurately understand the company, identify its expertise, find evidence supporting its claims, and confidently reference it when answering relevant questions.

A practical LLMO strategy should therefore combine:

• strong SEO fundamentals
• clear entity information
• original first-party evidence
• AI-friendly content architecture
• E-E-A-T
• authoritative third-party mentions
• structured and technically accessible websites
• systematic AI visibility measurement

Do not chase every new AI-search tactic as it appears. Build a digital presence that is easy to discover, easy to understand, easy to verify, and worth citing.

That is the foundation of sustainable visibility in both traditional search and AI search.