What Are GEO and LLMO? How to Build a Site That Gets “Cited” in the Age of AI Search

AI-powered search — ChatGPT, Google’s AI Overviews, Perplexity, and more — is spreading rapidly.

In a survey conducted in November 2025, a combined 64% of generative-AI users said they were satisfied with the AI summaries shown in search results and stopped researching further without clicking through to a link (Source: Mobile Society Research Institute).Another study found that a combined 23.9% of users completed their search using only the generative AI’s answer or additional AI follow-up questions alone (Source: ONEDER).

Until now, driving traffic to a website has depended on “ranking high in search results.” But the spread of this kind of “zero-click search” has created a new challenge for corporate websites: if a company isn’t cited by AI, it risks losing its very point of contact with users.

This is why the concepts of GEO (Generative Engine Optimization) and LLMO (Large Language Model Optimization) are drawing attention. This article explains what GEO and LLMO are, how they differ from SEO, and concrete measures you can start on today — backed by supporting data.

The Difference Between SEO and GEO/LLMO

SEO (Search Engine Optimization) refers to efforts aimed at ranking highly in search engines like Google.

GEO and LLMO, on the other hand, are approaches designed to make it more likely that generative AI will cite or reference your site as a “trustworthy source” when composing its answers. The objectives are different.

Traditional SEO has centered on:

• Raising search rankings
• Increasing click counts
• Increasing traffic to the website

GEO and LLMO, by contrast, focus on:

• Whether the information is structured in a way AI can easily understand
• Whether there is information that’s easy to cite as evidence for an answer
• Whether the site is recognized as a trustworthy source

This doesn’t mean SEO becomes unnecessary. Going forward, designing a “site that AI chooses” — in addition to SEO — will be key to competitiveness.

The experience rate of generative AI usage grew from 3.4% in March 2023 to 56.3% in a June 2026 survey (Source: Nippon Research Center) — approaching half of the target population in roughly three years.

Amid this shift, if a company’s website is never cited in an AI’s answer, users may finish their research without ever learning the company exists. This affects content marketing and lead generation as well.

Not just “being clicked,” but “being cited by AI” is becoming an important KPI in its own right.

What Kind of Sites Does AI Cite?

None of the generative AI providers has published the details of its citation logic, but reliable sources currently point to the following factors:

1. Information Architecture

Well-organized information with a clear heading structure. Pages that state the answer to a question clearly at the outset are easier for AI to process when summarizing or citing. The “inverted pyramid” style — stating the conclusion first, then following with details — is a writing approach repeatedly recommended in guides on AI search optimization.

2. E-E-A-T

“Experience,” “Expertise,” “Authoritativeness,” and “Trustworthiness” — the qualities Google emphasizes — are considered important in AI search as well. Who wrote the content, what expertise it’s based on, and whether it cites trustworthy sources are all thought to factor into evaluation.

3. Structured Data (schema.org / JSON-LD)

Implementing schema.org structured data — such as FAQPage, HowTo, and Article — via JSON-LD makes it easier for AI to mechanically grasp a page’s content and entities (company names, product names, people, etc.).

One important point to get right here: Google has not officially recognized structured data itself as a direct ranking factor. 

Google Search Central’s own documentation states clearly that if there’s a problem with structured data and a manual action is taken, the page will simply stop appearing as a rich result — it will not affect the page’s actual ranking in Google web search (Source: Google Search Central). 

Google also narrowed FAQ rich results in August 2023 — citing a desire to simplify search results — to a limited set of highly authoritative sites such as government and healthcare organizations, and in May 2026 eliminated FAQ rich results entirely (Source: Search Engine Journal). The era in which “if you implement it, it’s guaranteed to stand out” is already over.

In other words, the value of structured data isn’t in directly boosting rankings — it functions as a “guide rail” that helps AI read a page’s content without misunderstanding it. Since implementation cost is relatively low, a practical priority order is to start with Organization, Article, and FAQPage. Note that BreadcrumbList is considered to offer relatively little benefit for AI comprehension, so it can reasonably be given lower priority.

4. llms.txt

“llms.txt” — a Markdown-format file for communicating site structure to AI, proposed in 2024 by Answer.AI’s Jeremy Howard — has also been seeing wider adoption in 2026.

That said, Google Search’s John Mueller has publicly stated that llms.txt is unlikely to be a decisive factor in how AI chooses its reference sources (Source: Google Search Central). Indeed, as of 2026, no solid evidence confirms that llms.txt increases citation rates when the primary goal is being cited in AI search. Since it costs little to set up and carries no risk, it’s worth having “as insurance” — but expecting it alone to increase citations is unwise. What ultimately determines whether you get cited is the substance, structure, and trustworthiness of your actual content.

5. Core Web Vitals (CWV) and User Experience

Page load speed, usability, and other aspects of user experience remain an important evaluation axis. Even as AI search spreads, whether an actual visitor finds the site easy to use continues to matter.

6. Original/First-Hand Information, Unique Insight, and Track Record

AI is thought to place weight not just on generic statements repeated across many sites, but also on a company’s own unique knowledge and case studies. Original content — actual case studies, survey findings, expert perspectives — helps differentiate a company from its competitors.

A GEO/LLMO Checklist You Can Start On Today

GEO and LLMO don’t require a large-scale technical overhaul to get started. The first step is simply making your own site easier for both AI and people to understand.

• Are you inadvertently blocking AI crawlers (GPTBot, ClaudeBot, PerplexityBot, etc.) via robots.txt or CDN/WAF settings?
• Is your body text and structured data present in the HTML before JavaScript executes (since most AI crawlers don’t execute JavaScript)?
• Does the opening of each article concisely present the conclusion/summary that answers the user’s question?
• Are your headings (H1–H3) logically organized, with one topic per heading?
• Have you implemented high-priority structured data — FAQPage, Article, Organization — via JSON-LD (understanding the goal is aiding AI comprehension, not boosting rankings)?
• Do you clearly state the expertise and background of the author/supervising editor (E-E-A-T)?
• Do you cite sources and the basis for your data, and share first-hand information or your own case studies?
• Are you considering an llms.txt file as “insurance,” while treating the quality of the body content as the real core of your citation strategy?
• Are you maintaining basic user experience (CWV) such as page load speed?

What matters most is not assuming “we’re fine because we do SEO” or “we’re fine because we set up llms.txt.” As generative AI search becomes widespread, the role expected of websites is starting to change.

Conclusion

In the age of AI search, competitiveness is increasingly determined not just by search ranking, but by whether a site is one that AI wants to cite.

At the same time, individual technical measures like llms.txt and structured data still have aspects whose effectiveness hasn’t been confirmed, and on some of these Google itself has expressed a cautious view. Rather than being swayed by excessive expectations or overly definitive claims, the realistic approach to GEO/LLMO as of 2026 is to steadily focus on improving the quality, structure, and trustworthiness of your content as the core strategy, while supplementing it with low-cost technical measures.

The statistics and views in this article are based on publicly available information as of July 2026. Since the specifications of generative AI providers and search algorithms are subject to change, please check the latest information before implementing any measures.

Is Your Site Ready to Be Cited in AI Search?

That said, objectively judging on your own whether your site currently has a structure that’s easy for AI to understand, or content that’s easy to cite, isn’t a simple task.

Neuromagic offers a “GEO/LLMO Quick Diagnosis” service that evaluates your website from a GEO/LLMO perspective and organizes the improvements needed for the age of AI search. Based on factors covered in this article — information architecture, E-E-A-T, the state of your structured-data implementation, and more — we help clarify where you currently stand and which measures to prioritize.

If you find yourself wondering whether your site is ready for AI search, why not start by getting a clear picture of where you stand today?