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More people now ask ChatGPT, Claude, Perplexity, or a coding agent the questions they used to type into Google. Those AI tools read websites to build their answers, and most websites were never designed with an AI reader in mind.

That is the gap llms.txt tries to fill. It is a small text file that gives AI a shortcut to your best content, and since its proposal in 2024, it has become one of the most debated files in SEO. 

Some marketers call it “robots.txt for AI”, while Google’s John Mueller compared it to the keywords meta tag.

So which side is right? 

This guide explains what an llms.txt file is and how it works. It describes what goes inside one and what the latest data says about its effect on traffic. You will also see real examples, a step-by-step setup, and a simple way to decide whether your site needs an llms.txt file or not.

Let’s get started.

Key takeaways

• An llms.txt file is a plain-text Markdown file at yoursite.com/llms.txt that gives large language models a short summary of your site and links to your most useful pages.
• It guides AI tools when they answer a question. It does not block crawlers, control AI training, or replace robots.txt and your XML sitemap.
• Google says the file will neither help nor hurt your rankings in Google Search, and studies covering thousands of domains found no link between having one and getting cited by AI.
• The clearest benefit today is for developer docs, APIs, and SaaS products, where coding agents read documentation all day.
• It takes about an hour to create an llms.txt file, so adding one costs you little even while the payoff is still uncertain.

What is llms.txt?

An llms.txt file is a plain-text file, written in Markdown, that sits at the root of your website (for example, yoursite.com/llms.txt). It gives large language models (LLMs) a curated summary of your site along with links to the pages that matter most.

In simple words, it is a reading list for AI. Instead of making a chatbot or AI agent dig through menus, pop-ups, scripts, and hundreds of URLs, you point it straight to the pages that best answer questions about your business.

The idea came from Jeremy Howard, co-founder of Answer.AI, who published the llms.txt proposal on September 3, 2024. The proposal is now on version 2, which its authors say reflects two years of real-world adoption by sites worldwide.

As for the name, “LLMs” stands for large language models, the technology behind ChatGPT, Claude, and Gemini, and “.txt” means it is a plain text file. That is why you will also see people search for “llm.txt” or “llms txt.” They all refer to the same file.

The file caught on quickly in answer engine optimization circles because it promises something every marketer wants: more say in how AI tools understand and describe their brand.

What llms.txt is not

Many people get confused between robots.txt and llms.txt, even though these are two separate types of files.

An llms.txt file:

• Does not block or allow crawlers: Rules like “User-agent: GPTBot” and “Disallow: /” belong in robots.txt. Adding them to llms.txt does nothing, because the format has no access rules at all.
• Is not a replacement for your sitemap: The proposal itself says a sitemap covers too much content and leaves out LLM-friendly pages, so the two files serve different readers.

So what is it for? Web pages are built for people, full of menus, scripts, and ads, while AI models can only read a limited amount of text at once. An llms.txt file gives them a short, clean index of your key pages, and the proposal also suggests publishing Markdown copies of those pages so AI tools can skip the messy HTML altogether.

How does llms.txt work?

Llms.txt works as a shortcut at answer time. When an AI tool needs information from your website, it can open the file first, see what you cover, and fetch only the pages that fit the question.

Here is the typical flow:

1. A question comes in: A developer asks a coding assistant how to use your API, or a shopper asks a chatbot about your return policy.
2. The AI tool opens /llms.txt: The bot reads your one-line summary and the grouped list of links from your llms.txt file.
3. It fetches the right pages: It opens the linked pages that match the task, ideally clean Markdown versions with no layout clutter.
4. The bot answers with your context: If space in its context window runs short, it drops anything you placed under the “Optional” heading and tells the developer on the other side about all the critical details of the documentation or the page.

Version 2 of the proposal also added a way for pages to point to their llms.txt file, either with a link tag (rel=”describedby”) in the HTML head or an HTTP header. That makes the file easier for agents to discover while they browse your pages.

There is one catch. Nothing requires an AI system to look for the file. Whether llms.txt gets used depends entirely on the tool reading your site, and that is where most of the debate comes from.

The llms.txt format, explained with an example

The llms.txt format is plain Markdown in a set order. Only the first element is required, so even a two-line file is valid.

Here is an llms.txt example for a fictional outdoor gear store:

# Trailhead Outfitters

> Outdoor gear store shipping across the US. Free returns within 60 days.

Prices are in USD. Sizes follow US charts.

## Shop
– [Hiking boots](https://example.com/boots.md): Waterproof and lightweight styles
– [Size guide](https://example.com/size-guide.md): How to pick boot and jacket sizes

## Policies
– [Returns](https://example.com/returns.md): 60-day free returns and exchanges
– [Shipping](https://example.com/shipping.md): Delivery times and costs by state

## Optional
– [Our story](https://example.com/about.md): Founded in 2012

Notice what is missing. There is no blog archive, no tag pages, and no login links. A good llms.txt file is a short list of pages that answer real customer questions, each with a description that tells the AI what the page is for.

Defining llms-full.txt

An llms-full.txt file is a companion file that puts the full text of your key content into one large Markdown document instead of linking out to separate pages. Developers often paste llms-full.txt links into ChatGPT or Claude to load a product’s entire documentation in one go.

The trade-off is size. 

Because it holds entire sets of documentation, an llms-full.txt file can grow larger than many AI tools can handle in a single conversation. For most marketing websites, a lean llms.txt file is enough. You only need an llms-full.txt file for your API references and technical documentation.

Markdown versions of your pages

Jeremy Howard’s proposal also suggests offering a clean Markdown copy of each important page at the same URL with .md added, such as /pricing.md. 

Documentation platforms like GitBook and Mintlify now create these automatically. GitBook reports that AI systems made up over 40% of readers of GitBook-hosted docs by the end of 2025.

What’s the difference between llms.txt vs robots.txt vs sitemap.xml?

All three files live at the root of your site, but each one does a different job. 

Robots.txt controls where crawlers can go, sitemap.xml lists the URLs you want search engines to index, and llms.txt points AI tools to your most useful content.

If you want a refresher on how crawling, indexing, and ranking fit together, our guide on how search engines work walks through each stage in detail.

Who uses llms.txt? Real-life examples

Adoption started in developer documentation and spread from there. Mintlify switched llms.txt on for every docs site it hosts in November 2024, which brought Anthropic, Cursor, and many others on board almost overnight. 

In June 2025, Yoast SEO 25.3 added automatic llms.txt generation, putting the file one click away for WordPress sites.

Here are some llms.txt examples worth studying:

Stripe’s file is the most interesting of the group because it shows that llms.txt can carry instructions for AI coding assistants, not only links. 

According to an analysis by Apideck, the file at docs.stripe.com/llms.txt includes lines such as “Always use the Checkout Sessions API over the legacy Charges API.” These instructions help coding assistants give developers correct advice about Stripe’s current API versions.

Across the wider web, adoption is growing but still small. A crawl of the top one million domains found that 3.53% served a valid llms.txt in August 2026, up from 1.22% a year earlier. 

Among the top 300 sites, the share of the sites that have created and implemented an llms.txt file rose from 4% to 9%. In other words, more than 90% of the web’s biggest sites still do not have one.

Does llms.txt actually work? What the data says

There is no evidence yet that llms.txt improves Google rankings or AI citations. Most AI crawlers rarely request the file, and the readers it does attract are mostly coding agents and SEO tools.

Can an llms.txt file help SEO?

Not for Google. In its guide to optimizing for generative AI features, Google says you “don’t need to create new machine-readable files, AI text files, markup, or Markdown” to appear in its AI features, because Google Search does not use them. 

The article adds that creating llms.txt files will “neither harm nor help your site’s visibility or rankings in Google Search.”

That matches what John Mueller said in a Reddit thread on the topic:

His point is that llms.txt is only what a site owner claims about their site, and an AI system can check the site itself. That’s why clear page content and structured data like schema markup matter more.

Does llms.txt get you cited in AI answers?

So far, no. 

In November 2025, SE Ranking analyzed around 300,000 domains and found llms.txt on 10.13% of them, with no measurable link between having the file and how often AI platforms cite a domain. 

In SE Ranking’s words, the file “doesn’t seem to directly impact AI citation frequency. At least not yet.”

Do AI crawlers even read llms.txt?

Rarely. Ahrefs studied 137,210 websites in May 2026. They found that 28% published a valid llms.txt file (an upper bound, since Ahrefs’ sample leans toward SEO-aware sites), and 97% of those files got zero requests that month.

Of the requests that did come in, 96% came from bots. GPTBot was the most active AI bot, yet it made only 4.51% of all requests, and Slackbot fetched llms.txt files more often than PerplexityBot. 

Ahrefs also found that AI bots never requested llms.txt files from sites that didn’t have one, which led it to conclude that publishing one “does not put you on any AI radar.”

So why are sites still adding llms.txt?

There are three reasons why sites are still adding llms.txt:

• Coding agents use it: In the Ahrefs study, Claude Code was the second most active AI fetcher after GPTBot, ahead of every AI search and assistant bot. Developers also share llms.txt and llms-full.txt links with AI tools on purpose for better site data extraction.
• Tools are starting to check for it: Chrome’s Lighthouse now runs an llms.txt audit under its agentic browsing checks, and the major AI labs publish llms.txt files for their own developer docs.
• It costs almost nothing: It takes an hour of work and carries no ranking risk, and you are ready if AI platforms start reading it at scale.

The last reason is probably why developers add llms.txt files. It functions as future-proofing of your platform, and adding it isn’t tough anyway.

Should you create an llms.txt file?

For most websites, yes, as long as you treat it as a low-cost add-on and not a traffic strategy. It is most useful when AI tools need precise, current information from you, like API docs, product specs, pricing, or policies.

Add one if:

• You run developer docs, an API, or a SaaS product that people integrate with AI coding tools.
• Your pricing, policies, or product details change often, and AI answers about them are going stale.
• Your CMS or docs platform can generate and update the file for you.

You can skip it for now if:

• You are a local business, where your Google Business Profile, reviews, and schema matter more.
• Your site has a few pages and no technical content, so AI tools can read the whole thing anyway.
• You have bigger gaps to fix first, such as AI crawlers blocked in robots.txt or key content that only loads through JavaScript.

Risks with adding llms.txt

1. It makes your best content easier to copy: The file is public, so the same shortlist that guides AI tools also shows scrapers and competitors exactly where your most valuable pages are.
2. Outdated links lead to wrong answers: If the file still points to old pricing or retired docs, AI tools may repeat that outdated information to your customers.
3. AI agents may act on what it says: Agents read the file as instructions, so a careless edit can steer them in the wrong direction. Treat llms.txt like code and review every change before it goes live.

How to create an llms.txt file in 6 steps

You do not need a developer to create an llms.txt file. If you can edit a text file and upload it to your site, you can have one live in about an hour.

Step #1: Pick the pages that matter

Start with 10 to 15 links that matter most for you. Good candidates are product or service pages, pricing, getting-started guides, key documentation, policies, and your strongest evergreen pages. 

Leave out gated pages, duplicates, thin tag archives, seasonal content, and anything that goes stale within weeks.

Step #2: Write the file in Markdown

Open a plain text editor such as Notepad, TextEdit, or VS Code and save a file named llms.txt in lowercase. Add your H1 name, a blockquote summary, any context notes, and H2 sections of links with short descriptions. Move nice-to-have pages under an “Optional” heading.

Step #3: Add Markdown versions of key pages (optional)

If your platform supports it, publish .md copies of the pages you link to and point to those in your file. This step matters most for documentation. For a marketing site, linking to normal HTML pages is fine.

Step #4: Upload it to your root directory

Place the file in your site’s root folder (often public_html) using your host’s file manager or FTP, so it loads at yoursite.com/llms.txt. 

On WordPress, Yoast SEO and AIOSEO can generate and update it for you, and Wix, GitBook, and Mintlify offer built-in support too. The llms.txt proposal also lists community plugins for Docusaurus, VitePress, and Drupal.

Step #5: Test it

Open the URL in your browser. It should show plain text with a 200 status code. Click a few links to confirm they work, then run Chrome’s Lighthouse to see if its llms.txt check flags any server errors.

Step #6: Monitor and update

Check your server logs every few weeks for requests from bots like GPTBot, ClaudeBot, OAI-SearchBot, and PerplexityBot. Update the file whenever you launch a product, change pricing, or retire a page.

Whichever generator you use, read the output before publishing. Automatic tools tend to list everything, which defeats the purpose of a curated file.

Useful llms.txt best practices

A short, accurate llms.txt file beats a long one every time. These habits keep yours useful:

• Write descriptions that explain the purpose of the page: A description like “Pricing: plans, limits, and free trial details” tells an AI far more than “Pricing page.”
• Put your most important facts in the summary: Say what you do, who you serve, and any details that change answers, such as currency, regions, or plan names in the excerpt.
• Link only to live, canonical URLs: Skip redirects, tracking parameters, and duplicate versions of the same page.
• Keep facts consistent with your website: Make sure the prices, features, and plan names in the file match your live pages.
• Save the “Optional” section for background pages: Put pages there, such as your company story or older guides, so they are the first thing an AI drops.
• Add instructions if AI keeps getting you wrong: You can tell AI agents which features are current and which are retired, modified, or updated recently.
• Never include any private information: Anyone can open the llms.txt file in a browser, so keep internal tools, staging links, and customer data out of it.
• Review it on a schedule: A quarterly check, plus an update after every launch, keeps the file from drifting out of date.

Where llms.txt fits in your AI search strategy

llms.txt is one small, technical piece of generative engine optimization. The factors that decide whether ChatGPT, Perplexity, or AI Overviews mention your brand are much bigger.

That includes content that answers questions directly, pages AI crawlers can access, structured data, and mentions on sites that AI systems already trust.

Then there is measurement. The only way to know whether any of this works is to track how often AI answers mention, cite, and describe your brand, which is your AI visibility.

As an AEO tool, Contentpen helps you research topics and write SEO and GEO-optimized articles built to win SERP rankings and AI citations.

Its AI visibility tool then tracks your brand mentions, sentiment, and share of voice across up to 7 AI platforms, including ChatGPT, Gemini, Perplexity, and Claude. 

So instead of guessing whether a file like llms.txt changed anything, you can see exactly where your brand shows up in AI answers.

Also read: Our roundup of the best AI search optimization tools in 2026.

Final thoughts

Creating an llms.txt file gives AI a clean, curated guide to your website. It is cheap to create, easy to maintain, and useful today for developer docs and AI coding tools. 

For rankings and AI citations, the evidence so far says it will not move results on its own. You can add it easily and measure the before-and-after effects to keep the llms.txt file or remove it. The decision is yours.

To get your brand cited in AI answers, you need content that deserves to be cited, backed by stats, and perfectly formatted for AI parsing. This is where Contentpen helps businesses put content creation, optimization, and publishing on Autopilot.

Write content that’s built to rank, not just read

Create search-optimized blogs aligned with SEO and GEO signals, so your content performs well across search and AI-driven discovery.

Try SEO Blogging FREE →

Try it free for 7 days and start winning AI citations and SERP rankings today.

Frequently asked questions

The main types of schema markup are Organization, LocalBusiness, Person, WebPage, Breadcrumb, Article, Video, FAQ, HowTo, Recipe, Product, Review, AggregateRating, Event, JobPosting, Course, and Dataset. Schema.org defines 826 types in total, but these 25 cover nearly every website. Each one labels a specific kind of content so search engines know exactly what they’re looking at.

So why do so many sites still get schema wrong? Most lists online haven’t kept up with Google’s changes.

That means people keep adding FAQ and HowTo markup expecting rich results that Google no longer shows. Meanwhile, useful types like ProfilePage or Dataset get skipped entirely.

This guide walks through 25 types of schema markup one by one. For each, you’ll see what it does, where it belongs, the key properties, and its 2026 status in Google.

Key takeaways

• Schema.org has 826 types, but 25 common schema types cover almost every website’s needs.
• Most sites only need five to eight types, starting with Organization, WebSite, and Breadcrumb.
• Google stopped showing HowTo rich results in 2023 and FAQ rich results in May 2026, though both are still valid code.
• Some types, like Person, WebPage, and Service, never earn a rich result but still help search engines understand entities.
• Product, Review, and AggregateRating work together to show prices and stars in search.
• No schema type is required for AI Overviews, but clear entity markup supports how search engines describe your brand.

How many types of schema markup are there?

It depends on which list you’re reading. Three numbers explain the whole picture.

Every schema.org type sits inside one family tree. It starts with Thing, then branches into groups like CreativeWork, Organization, Person, Place, Product, and Event.

That tree matters.

A Bakery is a FoodEstablishment, which is a LocalBusiness, which is an Organization, so picking the most specific type gives you every property above it.

Quick reference: all 25 schema types and their 2026 status

Short on time? Scan this table first, then jump to the types of schema markup you need.

Site and entity schema types

These types describe who you are and how your site is organized. Most websites need several of them.

1. Organization schema

Organization schema describes your company as a whole. It’s the anchor that every other type on your site can point back to.

• Use it on: your homepage or about page.
• Key properties: name, url, logo, contactPoint, sameAs.
• Google status: supported, and it can help Google show your logo and company details.

The sameAs property is worth extra attention. It links your brand to official profiles like LinkedIn, which strengthens your entity.

2. Local business schema

LocalBusiness is a more specific kind of Organization for places customers can visit. It has hundreds of subtypes, such as Dentist, Restaurant, and Store.

• Use it on: each location page, one block per location.
• Key properties: address, geo, openingHoursSpecification, telephone, priceRange.
• Google status: supported for local business details.

Organization or LocalBusiness? Use Organization for the brand, and LocalBusiness for each physical branch. Link each branch back to its parent brand.

3. Person schema

Person schema describes an individual, usually an author, founder, or expert. It ties a name to real credentials and profiles.

• Use it on: author pages, team pages, and inside Article markup.
• Key properties: name, jobTitle, worksFor, sameAs, image.
• Google status: no rich result of its own, but it feeds author details and Profile page markup.

Person markup supports trust. A clearly identified author with real credentials beats an anonymous byline.

4. Web page schema

WebPage schema describes the page itself rather than its main subject. It acts as a hub that connects the article, the site, and the breadcrumb trail.

• Use it on: any page, often added automatically by SEO plugins.
• Key properties: name, url, isPartOf, breadcrumb, datePublished.
• Google status: no rich result, but useful for connecting entities with @id.

Subtypes like AboutPage, ContactPage, and FAQPage tell search engines what kind of page it is.

5. WebSite schema

WebSite schema describes your whole site. Google uses it to choose the site name shown above your search results.

• Use it on: your homepage only.
• Key properties: name, alternateName, url.
• Google status: supported for site names.

It’s also the most common type online. The HTTP Archive Web Almanac 2024 found WebSite JSON-LD on 12.73% of mobile pages, ahead of Organization at 7.16%.

6. Breadcrumb schema

BreadcrumbList shows the path from your homepage to the current page. Google can display that trail in place of a raw URL.

• Use it on: almost every inner page.
• Key properties: itemListElement, with position, name, and item for each step.
• Google status: supported.

Breadcrumbs pair nicely with good internal linking, since both show how your pages connect.

Content schema types

These types describe what a page says or shows. Publishers, bloggers, and media sites rely on them most.

7. Article schema

Article schema tells search engines a page is a written piece, with a headline, an author, and dates. Use BlogPosting for blogs and NewsArticle for news.

• Use it on: blog posts, news stories, and guides.
• Key properties: headline, image, author, datePublished, dateModified, publisher.
• Google status: supported.

Article markup works best when the piece is already well organized. It’s worth learning how to structure a blog post before tagging it.

8. Video schema

VideoObject schema helps Google show your video’s thumbnail, length, and upload date. Longer videos can also get key moments.

• Use it on: any page where a video is the main content.
• Key properties: name, description, thumbnailUrl, uploadDate, duration, contentUrl.
• Google status: supported.

9. FAQ schema

FAQPage schema marks up a list of questions and answers. For years it produced expandable questions under your listing.

• Use it on: pages with a real FAQ section written by you.
• Key properties: mainEntity, with Question and acceptedAnswer pairs.
• Google status: retired. FAQ rich results stopped showing for all sites in May 2026.

Should you still use it? The code stays valid, so it won’t hurt. The visible FAQ section still helps readers and answer engines, so keep the content either way.

10. How to schema

HowTo schema describes step-by-step instructions, with tools, supplies, and time needed for each step.

• Use it on: tutorials and step-by-step guides.
• Key properties: name, step (HowToStep), totalTime, tool, supply.
• Google status: retired. HowTo rich results stopped showing on desktop and mobile in September 2023.

Clear numbered steps in plain HTML still work well. Readers and AI tools can follow them, markup or not.

11. Recipe schema

Recipe schema is one of the oldest and most visual types. It can show cook time, ratings, and calories right in search.

• Use it on: individual recipe pages.
• Key properties: name, image, recipeIngredient, recipeInstructions, cookTime, nutrition.
• Google status: supported, including recipe carousels.

12. Book schema

Book schema describes a book’s title, author, and editions. Google also supports Book actions, which let people borrow or buy a book from search.

• Use it on: book pages from publishers, bookstores, and libraries.
• Key properties: name, author, isbn, bookFormat, workExample.
• Google status: limited. Google planned to retire Book actions in 2025, then reversed that in November 2025.

13. Profile page schema

ProfilePage schema marks a page that’s mainly about one person or organization. It’s a strong signal for creator and author pages.

• Use it on: author bios, creator profiles, and user profile pages.
• Key properties: mainEntity (a Person or Organization), dateCreated, dateModified.
• Google status: supported.

14. Discussion forum schema

DiscussionForumPosting describes threads and posts on forums and community sites. It helps Google surface real conversations.

• Use it on: forum threads, community posts, and comment-led pages.
• Key properties: headline, author, datePublished, text, comment.
• Google status: supported.

15. Q&A schema

QAPage schema marks a page built around one question with answers from users. Think support communities, not your own FAQ list.

• Use it on: pages where users submit answers to a single question.
• Key properties: mainEntity (Question), acceptedAnswer, suggestedAnswer, answerCount.
• Google status: supported.

Not sure whether you need Q&A or FAQ? If you wrote all the answers, it’s FAQ. If users answer, it’s Q&A.

16. Image metadata schema

ImageObject markup tells Google who created an image and how it can be licensed. That can add a “Licensable” badge in Google Images.

• Use it on: photography sites, stock libraries, and original image pages.
• Key properties: contentUrl, license, acquireLicensePage, creator, creditText.
• Google status: supported.

Product, review, and service schema types

These types matter most for stores, SaaS brands, and service businesses. Price, stock, and stars in a listing can tip a buyer’s choice before they click.

17. Product schema

Product schema describes an item you sell. Combined with an Offer, it can show price and availability in search and in Google Shopping.

• Use it on: individual product pages, not category pages.
• Key properties: name, image, description, SKU, brand, offers (price, priceCurrency, availability).
• Google status: supported for product snippets and merchant listings, including shipping and return details.

The payoff can be real. In Google’s structured data case studies, Rotten Tomatoes saw a 25% higher click-through rate on pages with structured data.

18. Product review schema

Review schema marks up a single review of a product, with the reviewer and their rating. Editorial review sites use it heavily.

• Use it on: product review articles and individual customer reviews.
• Key properties: itemReviewed, author, reviewRating, reviewBody.
• Google status: supported as review snippets.

One rule to remember: reviews a business writes about itself aren’t eligible for stars. Keep it to genuine, independent reviews.

19. Aggregate rating schema

AggregateRating summarizes many reviews into one average score and count. It’s what produces the “4.7 stars from 128 reviews” line.

• Use it on: products, recipes, software, and other items with real ratings.
• Key properties: ratingValue, reviewCount or ratingCount, bestRating.
• Google status: supported inside review snippets.

AggregateRating never stands alone. Nest it inside the Product, Recipe, or SoftwareApplication it describes.

20. Software schema

SoftwareApplication schema describes apps, games, and SaaS tools. It can show ratings and price in search.

• Use it on: app landing pages and software product pages.
• Key properties: name, operatingSystem, applicationCategory, offers, aggregateRating.
• Google status: supported.

SaaS brands often overlook this one. It’s a natural fit for pricing and product pages.

21. Service schema

Service schema describes something you do rather than something you sell in a box. Think SEO audits, plumbing, or legal advice.

• Use it on: individual service pages.
• Key properties: serviceType, provider, areaServed, offers.
• Google status: no rich result, but it clarifies what your business offers.

Agencies and consultants get the most from it. Pair each Service with your Organization or LocalBusiness as the provider.

Event, job, and education schema types

These types fit content tied to a date, a role, or a learning goal. Each has its own dedicated Google experience.

22. Event schema

Event schema describes something happening at a set time, online or in person. It can surface in Google’s event listings.

• Use it on: event pages for concerts, webinars, workshops, and classes.
• Key properties: name, startDate, endDate, location, eventStatus, eventAttendanceMode, offers.
• Google status: supported.

Update eventStatus if plans change. Google can then show an event as postponed or cancelled.

23. Job posting schema

JobPosting schema puts open roles into Google’s job search experience. It’s one of the most valuable types for hiring.

• Use it on: each individual job listing page.
• Key properties: title, description, datePosted, validThrough, hiringOrganization, jobLocation, baseSalary.
• Google status: supported.

Remove the markup, or set validThrough, once a role is filled. Expired listings frustrate applicants.

24. Course schema

Course schema describes a class or training program. Google now uses it for course lists, which show several courses from one provider.

• Use it on: course catalog and course detail pages.
• Key properties: name, description, provider, hasCourseInstance, offers.
• Google status: supported for course lists. The separate Course info result was retired in 2025.

25. Dataset schema

Dataset schema describes data tables, research files, and open data. It helps your data appear in Google Dataset Search.

• Use it on: pages hosting downloadable or public datasets.
• Key properties: name, description, creator, license, distribution, temporalCoverage.
• Google status: supported in Dataset Search.

It’s a niche type, but a powerful one. Research teams and publishers with original data should never skip it.

Which schema types has Google retired?

Google has spent the last few years trimming features that rarely helped searchers. Every entry below comes from Google’s own Search Central updates log.

Here’s the key point. Retired features don’t make the code invalid. They simply stop producing a special listing in Google.

Which types of schema markup should your site use?

You don’t need all 25. Use this table as a starting kit for your kind of site.

Start with the must-haves on your top pages. Then roll out the extras once the basics validate cleanly.

Schema is only one piece of on-page SEO, so don’t skip titles, headings, and content quality along the way.

Which schema types matter most for AI search?

The short answer: the ones that define your entity. Organization, Person, Product, and LocalBusiness tell search systems exactly who and what you are.

Google is clear that you don’t need any special markup to appear in AI features. Its AI features guidance says there’s “no special schema.org structured data that you need to add.”

Still, consistent entity data helps search engines connect your brand across the web. That supports AI search optimization and the brand mentions behind citation SEO.

The best way to know if it’s paying off? Track your AI visibility before and after you roll out new markup.

How Contentpen supports a schema-ready content strategy

Markup describes your content, but it can’t fix weak content. Contentpen, an AI SEO tool, helps you build pages that each schema type can describe cleanly.

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Here’s how it fits into your schema workflow:

• Structure built for extraction. The AI search optimization tool scores content with a real-time GEO score and adds key takeaways and a linked table of contents.
• Clean article basics. SEO scoring checks heading hierarchy, meta details, image alt tags, and named entities, the same fields Article markup relies on.
• Publishing to your CMS. Posts go straight to WordPress, Shopify, Webflow, and more, where your schema plugin adds the JSON-LD.
• Proof it’s working. Website analytics pulls in Search Console data, so you can watch clicks and CTR on pages with new markup.

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Fewer schema types, done well, beat a long list

There are 25 schema types worth knowing, but no site needs all of them. Chasing every type wastes time on markup that shows nothing.

Start with the site and entity types, add the ones that match your pages, and treat FAQ and HowTo as optional. Then validate, publish, and check the results in Search Console.

A short, accurate set of types will always outperform a long, messy one.

Frequently asked questions

Schema markup for AI optimization means using structured data to help AI search systems identify your brand, products, and authors without guessing. It won’t get you cited on its own.

Google says no special schema is needed for AI Overviews, and a 1,885-page study found almost no citation lift. However, schema still strengthens the search indexes and entity data that AI answers draw from.

Sounds confusing? You’re not alone.

Half the internet says schema is the secret to AI visibility, and the other half says AI ignores it.

That noise leads teams to spend weeks on markup and then wonder why ChatGPT still doesn’t mention them. Or they skip schema entirely and leave their entity data a mess.

This guide sorts the evidence, explains how AI systems actually treat schema, and gives you a seven-step plan plus a way to test it.

Here’s what we know in 2026.

Key takeaways

• Schema markup supports AI optimization indirectly, mainly by making entities clear to search indexes and knowledge graphs.
• Google states there is no special schema required to appear in AI Overviews or AI Mode.
• A controlled Ahrefs study of 1,885 pages found AI citations barely moved after adding schema.
• LLMs that fetch pages live tend to read JSON-LD as plain text, valid or not.
• Organization, Person, Product, and LocalBusiness, linked with sameAs and @id, matter most.
• Test it yourself with control pages and AI visibility tracking instead of trusting vendor claims.

What is schema markup for AI optimization?

It’s the practice of using schema.org markup to make your content easier for AI-powered search to understand. The goal isn’t a rich result. It’s entity clarity. It’s one small part of generative engine optimization.

When an AI system builds an answer, it needs to know who you are and what you sell. It also needs to know which facts on your page are current.

Schema states those facts in a fixed format. Think of it as the fact sheet stapled to your content. It sits beside the broader work of answer engine optimization, which focuses on answers worth quoting.

How do AI search engines use schema markup?

Here’s where most guides get it wrong. “AI search” isn’t one system, and schema plays a different role in each path.

The third path is why schema still matters. Google’s AI Overviews and Bing Copilot lean on the same indexes that have used structured data for years.

What does the evidence actually say?

Let’s look at the sources, not the hype. This table pulls together the main statements and studies so far.

So what’s the verdict? Schema isn’t a citation switch. It’s infrastructure that helps the search side of AI understand you.

Does ChatGPT read JSON-LD?

Sometimes, but probably not the way you’d hope. In the Williams-Cook test, ChatGPT pulled a fact from broken JSON-LD, which suggests it read the code as text.

That means it may notice what’s in your markup. It just doesn’t seem to reward valid schema over the visible content on your page.

Do you need special schema for AI Overviews?

No. Google’s guidance says AI Overviews and AI Mode have no extra technical requirements beyond normal Search eligibility.

It does list one structured data best practice: your markup should match the visible text on the page. Accuracy beats volume.

Which schema types matter most for AI search?

Focus on schema types that define who and what you are. These carry the entity facts AI answers repeat most.

What about FAQPage? Google stopped showing FAQ rich results in May 2026. A clear, visible FAQ section is still worth keeping, though, because AI tools quote well-formed questions and answers.

How to use schema markup for AI optimization in 7 steps

Ready to put this into practice? Follow these steps in order.

1. Audit your current markup: Run key pages through the Rich Results Test and the Schema Markup Validator. Note errors, duplicates, and missing types.
2. Define your core entities: List your brand, key people, products, and locations. Each one should get a single, authoritative page.
3. Mark up each entity once, properly: Add Organization on the homepage, Person on author pages, and Product or LocalBusiness where relevant.
4. Connect entities with @id and sameAs: Give each entity a stable @id and link it to its official profiles. This builds one consistent identity.
5. Match markup to visible content: Every fact in your JSON-LD should appear on the page, with the same price, name, and dates.
6. Keep facts fresh: Update dateModified and prices whenever the page changes, and never fake freshness.
7. Measure before and after: Track the AI prompts you care about for a few weeks before the change and a few weeks after.

This entity-first approach is one of the biggest differences in AI search optimization vs. traditional SEO.

How can you test whether schema helps your AI visibility?

Don’t take anyone’s word for it, ours included. Run a simple test on your own site.

• Pick matched pages: Choose 10 to 20 similar pages and add schema to half of them. Leave the other half as your control group.
• Track the same prompts: Monitor the questions buyers ask in ChatGPT, Perplexity, Gemini, and Google AI Mode.
• Wait, then compare: Give it at least four weeks, then compare citation changes between the two groups.
• Check live retrieval: Put a harmless, unique detail only in your JSON-LD, then ask an AI tool about it.

A structured AI visibility audit gives you the baseline to make this comparison fair.

When does schema matter most for AI?

Not every site gains the same amount. Schema tends to matter more in a few situations:

• New or small brands that search engines don’t know yet.
• Ambiguous names that clash with other companies, products, or people.
• Product catalogs where price and stock change often.
• Local businesses with several locations or service areas.

It matters less for well-known brands that already have a clear knowledge panel. That may be why the Ahrefs study, which tested pages already being cited, saw so little change.

What mistakes hold back schema for AI?

These errors waste effort or, worse, send conflicting signals.

• Treating schema as a shortcut: Markup can’t make thin content quotable.
• Mismatched facts: A price in JSON-LD that differs from the page confuses every system that reads both.
• Fake freshness: Bumping dateModified without real changes erodes trust.
• Inconsistent entity names: Using “Brand Inc.” in one place and “Brand” in another splits your identity.
• Ignoring off-site signals: AI answers lean heavily on mentions elsewhere, which is why share of voice in AI results matters as much as your own code.

How Contentpen helps you measure AI optimization

Schema is only worth the effort if you can see whether it moves anything. Contentpen, an AEO tool built for SEO and AI search, gives you that visibility.

Find your brand in AI search

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• Track AI mentions across seven engines: The AI visibility tool monitors ChatGPT, Google AI, Copilot, Perplexity, Gemini, Grok, and Claude for the prompts you choose.
• Write content worth citing: The GEO optimization tool scores drafts in real time and flags fixes, so the page under your schema is clear and extractable.
• Compare before and after: Shareable visibility reports and exportable analytics make your control-group test easy to run and report.
• Work from your AI assistant: The new Contentpen MCP server connects Claude, ChatGPT, Cursor, and other MCP-compatible assistants to your workspace.

Contentpen doesn’t generate schema for you. It shows whether your schema and content changes actually change what AI says about your brand.

AI Visibility is included in every plan. Check Contentpen pricing and start tracking your first prompts.

Schema is the label, your content is the reason

Schema markup won’t make AI cite you by itself. The best evidence in 2026 says it’s a supporting layer, not a growth lever.

What it does well is remove doubt. It tells search indexes exactly who you are, what you offer, and how your pages connect.

So mark up your core entities, keep the facts accurate, and spend most of your energy on content worth quoting. Then measure, and let your own data decide.

Frequently asked questions

SEO vs GEO vs AEO comes down to where you want to show up. SEO gets your page ranked in search results. AEO gets your content pulled out as the direct answer, and GEO gets your brand cited inside AI answers from tools like ChatGPT and Gemini.

All three share one foundation: useful, well-structured content that machines can trust. The difference is the surface you win on and how you measure the win.

Yet most marketers are being pitched these as three separate services, each with its own playbook and price tag.

That leaves most teams guessing. Do you rewrite everything for AI? Do you stop building links? Do you hire a “GEO specialist” and hope for the best?

You don’t need three strategies. You need one content system that ranks, answers, and earns citations at the same time.

This guide breaks down what each term means, where they overlap, how to measure them, and a 30-day plan to cover all three.

Let’s start with the basics.

Key takeaways

• SEO wins rankings and clicks, AEO wins the direct answer, and GEO wins citations in AI-generated responses.
• AEO is about being extracted. GEO is about being trusted enough to be one of the few sources an AI blends into its answer.
• GEO does not replace SEO. AI engines still pull from pages that are crawlable, indexed, and authoritative.
• AIO, LLMO, AI SEO, and LLM SEO are mostly new labels for parts of AEO and GEO.
• Each layer needs its own metrics: rankings for SEO, answer ownership for AEO, and citation share for GEO.
• One well-built page can serve all three if it leads with a clear answer, uses clean structure, and backs claims with sources.

What do SEO, AEO, and GEO each optimize for?

Think of them as three questions a search system asks about your content. Should this page rank? Is this the answer? Should I trust and cite this brand?

SEO, or search engine optimization: earning the ranked spot

SEO is the practice of getting your pages to rank in the organic results on Google and Bing. The goal is a click to your site.

It rests on three pillars:

• On-page work: keywords, headings, meta data, and content that matches what the searcher wants.
• Technical SEO: crawlability, speed, indexing, canonicals, and mobile usability.
• Off-page signals: backlinks, brand mentions, and overall authority.

AEO, or answer engine optimization: becoming the answer

AEO is the practice of structuring content so a search system can lift it out as the direct answer. That answer might be a featured snippet, a People also ask box, a voice reply, or a line inside Google AI Overviews.

The focus shifts from the whole page to the single passage. Short, self-contained answers under question-based headings tend to win here.

GEO, or generative engine optimization: becoming the cited source

GEO is the practice of making your brand and content the source that large language models cite when they write an answer. Think ChatGPT, Gemini, Perplexity, Claude, Copilot, and Google’s AI Mode.

The term comes from a 2023 research paper by Aggarwal and colleagues. They found that GEO methods could lift visibility in generative engine responses by up to 40%.

GEO cares about more than your own pages. It weighs how often trusted third parties mention you, how consistently your brand is described, and whether your content carries facts worth quoting.

How do SEO, AEO, and GEO compare side by side?

Let’s line them up. Full forms first, then the details that actually change your workflow.

The big takeaway?

The further right you move in the table, the less you control the final output. That’s why GEO leans so heavily on reputation built outside your own site.

Is GEO the same as AEO?

Not quite, though the line is blurry. Many people now use AEO as an umbrella term for all AI answers, which is where the confusion starts.

The cleanest way to split them is by what the system does with your content:

• AEO is extraction: The engine finds one passage on your page and shows it, often word for word.
• GEO is synthesis: The engine reads many sources, writes its own answer, and cites a few of them.

So AEO is mostly won on the page. GEO is won on the page and off it, through reviews, forum threads, directories, and press that mention you.

Want a concrete AEO vs GEO example? Picture someone asking, “What is a canonical tag?”

• AEO win: your 45-word definition appears as the featured snippet, exactly as you wrote it.
• GEO win: someone asks ChatGPT “best way to fix duplicate content issues,” and the answer cites your guide alongside two others.

Is GEO the same as SEO, or is it replacing it?

No, on both counts. GEO sits on top of SEO rather than replacing it.

The reason is simple. AI engines still need to find your content before they can cite it, and most of them lean on web search indexes to do that. If a page can’t be crawled or doesn’t rank anywhere, its odds of being cited drop sharply.

What changes is the scoring. Traditional SEO rewards relevance and links to a page, while GEO rewards how believable and quotable your brand looks across the whole web.

Where do AIO, LLMO, and AI SEO fit in?

If you’ve searched “SEO vs GEO vs AEO vs AIO” or “vs LLMO,” you’ve seen how fast the labels multiply.

The good news?

Most of them describe the same work from a slightly different angle.

Don’t let the alphabet soup derail your planning. If you cover rankings, extractable answers, and brand authority, you’ve covered every one of these.

Why does the difference matter for your traffic in 2026?

Because clicks are getting harder to earn when an AI summary sits on top of the results. The data so far points in one direction.

• A Pew Research Center study of 900 U.S. adults found users clicked a traditional result on 8% of visits when an AI summary appeared, versus 15% without one.
• In the same study, people clicked a link inside the AI summary itself on only about 1% of visits.
• Gartner predicted back in 2024 that traditional search engine volume would drop 25% by 2026 as people shift to AI chatbots and virtual agents.

What does that mean in practice?

Ranking alone no longer guarantees attention. You also need to be the answer, and the brand the AI names when it skips the click entirely.

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What does one page built for all three look like?

This is the part most comparisons skip. You don’t need three versions of a page. You need one page with three layers baked in.

Each part of the page pulls its weight in a different way:

One tip that pays off across all three: write each section so it makes sense on its own. AI systems often pull a single chunk, not your whole article.

How do you measure each one?

This is where teams go wrong most often. They judge GEO with SEO metrics, then decide it isn’t working.

A simple rule helps. Keep a single content plan, give each layer its own scorecard, and begin GEO with an AI visibility audit so you have a baseline.

Find your brand in AI search

Track AI citations, share of voice, and brand sentiment

Find the top sources driving citations

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Which one should your team prioritize first?

Start with SEO if your basics are shaky. Without crawlable, ranking pages, AEO and GEO have very little to work with.

After that, the right mix depends on how your buyers search:

Not sure where you stand?

Pick five questions your buyers ask. Search them on Google, then ask ChatGPT and Perplexity. Where you’re missing tells you where to start.

A 30-day plan to cover SEO, AEO, and GEO together

Ready to put this into motion? This is a realistic month for a small team.

Week 1: Baseline and fix the foundation

• Record rankings and clicks for your top 20 pages.
• Run 20 to 30 buyer prompts across AI tools and note who gets cited.
• Fix crawl errors, broken canonicals, and pages blocked by robots.txt.

Week 2: Rebuild your top pages for answers

• Add a direct answer in the first 100 words of each priority page.
• Turn vague headings into the questions people actually ask.
• Add a comparison table or step list wherever readers need to choose or act.

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Week 3: Add proof and structure

• Replace unsourced claims with stats linked to the original study.
• Add FAQ and article schema, plus real author bios.
• Tighten internal linking so related answers point to each other.
• Fill topic gaps using semantic SEO so each page covers the full subject, not just one keyword.

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Week 4: Build authority off your site

• Pitch one data point or expert quote to industry publications.
• Show up where AI engines look for opinions, using Reddit SEO and relevant communities.
• Get listed on trusted review sites and directories, a core part of citation SEO.
• Rerun your week 1 prompts and compare.

Don’t expect GEO to move in a month. Rankings and snippets can shift within weeks, but AI citations tend to follow authority, and authority takes longer.

How Contentpen helps you run all three from one workspace

Juggling a rank tracker, a writing tool, a schema plugin, and an AI monitoring tool gets messy fast. Contentpen is an SEO and AEO tool that brings that whole loop into one place.

It maps neatly to each layer:

• For SEO: find what to write with keyword research and topic clusters, then see what top pages cover with SERP and gap analysis. Google Search Console data sits right inside your workspace.
• For AEO: write in an editor with live SEO and GEO scoring. Failing checks can be fixed with one AI pass, so answers, headings, and structure stay tight.
• For GEO: AI Visibility tracks how ChatGPT, Google AI, Gemini, Perplexity, Copilot, Grok, and Claude mention your brand. It shows share of voice, sentiment, top cited sources, and the source gaps where competitors get cited and you don’t.

Once a draft is ready, automated linking and one-click publishing to WordPress, Shopify, Wix, and other CMSs take care of the busywork. Teams can also connect Contentpen to AI assistants through its API and MCP server.

Managing this for clients? See how Contentpen for agencies handles multiple brands in one account.

Create rank worthy content at scale

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SEO-ready

Efficient

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Want to see where you stand across search and AI?

Start with Contentpen today and track your rankings, answers, and AI citations in one place.

One search strategy, three ways to win

SEO, AEO, and GEO aren’t rivals. They’re three layers of the same job: making your content easy to find, easy to extract, and easy to trust.

SEO gets you ranked. AEO gets you chosen as the answer. GEO gets your brand named when an AI writes the answer itself.

Build one strong page at a time, measure each layer on its own terms, and keep earning mentions beyond your site. Do that, and the labels stop mattering.

Frequently asked questions

A schema markup is a short block of JSON-LD code that describes a page’s content using the schema.org vocabulary. The simplest one names your organization:

Wrap it in a script tag, paste it into your page, and search engines can read it.

The trouble is that most guides show schema as screenshots. You can’t copy an image, and retyping code by hand is how errors creep in.

Worse, many examples online are outdated. Some still promise FAQ rich results that Google stopped showing in 2026.

Below you’ll find 10 copyable schema markup examples, a connected multi-type example, and the steps to test them.

Grab the one you need and adapt it.

Key takeaways

• Every example here uses JSON-LD, the format Google recommends whenever your setup allows it.
• Start with Organization, WebSite, and BreadcrumbList, then add page-level types like Article or Product.
• Always fill Google’s required properties, or valid code still won’t qualify for rich results.
• Connect multiple types on one page with @graph and @id instead of separate, disconnected blocks.
• FAQPage code is still valid, but it no longer earns a rich result in Google Search.
• Test every snippet in the Rich Results Test and the Schema Markup Validator before going live.

What does a schema markup example look like?

Every JSON-LD example follows the same pattern. Once you see it, every other snippet becomes easy to read.

That’s the whole structure. Now let’s look at real examples.

10 schema markup examples in JSON-LD

Each snippet below includes the script tag, so you can paste it straight into a page. Swap the sample values for your own.

1. Organization schema example

Use this on your homepage or about page. The sameAs list ties your brand to its official profiles.

2. WebSite schema example

This one tells Google your preferred site name. Place it on the homepage only.

3. BreadcrumbList schema example

Breadcrumbs show the page’s path instead of a raw URL. Each step needs a position, a name, and a link.

The last item can skip the item URL, since it’s the current page.

4. Article schema example

Use BlogPosting for blog posts and NewsArticle for news. Keep dateModified honest, because stale dates erode trust.

Article markup works best on content that’s already well built. If you’re still drafting, learn how to write a blog post for SEO first.

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5. Product schema example

This is the snippet ecommerce stores ask about most. It combines Product, Offer, and AggregateRating in one block.

Only add ratings that come from real customer reviews shown on the page. Google’s product structured data guide lists the extra properties for merchant listings, such as shipping and returns.

6. LocalBusiness schema example

Use the most specific subtype you can, such as Dentist, Restaurant, or Store. Add one block per location page.

7. Event schema example

This works for in-person and online events. Always include eventStatus so you can mark changes like postponements.

8. Person and ProfilePage schema example

Use this on author pages. It links a writer to their real-world profiles, which supports expertise signals.

9. VideoObject schema example

Video markup helps Google show thumbnails and durations. The duration uses ISO 8601 format, so 4 minutes 30 seconds is PT4M30S.

10. FAQPage schema example (and its 2026 status)

Here’s a heads-up before you copy this one. Google’s Search Central updates log confirms FAQ rich results stopped showing in Google Search in May 2026.

The code is still valid schema.org, so it won’t cause errors. Just don’t expect expandable questions under your listing. The visible FAQ section on the page still helps readers and answer engines, as many AEO examples show.

How do you connect several schema types on one page?

Most pages need more than one type of schema. Instead of pasting three separate blocks, connect them in a single @graph and link them with @id.

Why bother? Each @id works like a name badge. Search engines see one connected story, with the article on the page, the page on the site, and the publisher behind it all.

Define #website with your WebSite block from example 2, given the same @id.

JSON-LD vs Microdata vs RDFa: the same example three ways

All three formats can describe the same organization. Here’s how each one looks.

JSON-LD keeps the code separate from your HTML:

Microdata adds attributes to tags you already have:

RDFa does the same with different attribute names:

Google’s introduction to structured data recommends JSON-LD where possible. It also shares results like Food Network’s 35% increase in visits after adding structured data to 80% of its pages.

Where should you paste schema markup?

JSON-LD can sit in the <head> or the <body>. Where you add it depends on your platform.

Render the code on the server whenever you can. Markup injected late by JavaScript can be missed or delayed. It’s one of those technical SEO basics that saves headaches later.

How do you test a schema markup example?

Never ship a snippet you haven’t tested. It takes two minutes.

1. Paste your code or URL into Google’s Rich Results Test to check rich result eligibility.
2. Run the same code through the Schema Markup Validator to catch general schema.org errors.
3. Fix any red errors first, then review the yellow warnings for missing recommended properties.
4. Publish, then request indexing in Search Console.
5. Watch the enhancement reports in Search Console over the next few weeks.

Once rich results appear, compare clicks before and after. Our guide on how to calculate CTR walks through the math.

What are the most common schema markup errors?

These are the errors that turn up most often when people adapt an example. Each one has a quick fix.

How Contentpen fits into your schema workflow

A perfect snippet can’t save a weak page. Contentpen, the SEO tool online for SEO and AEO teams, helps you build the page your schema describes.

• Better source material: The SEO scoring tool checks headings, meta details, alt tags, FAQ blocks, and named entities as you write. Those become the values in your Article markup.
• Clean handoff to your CMS: Contentpen’s CMS integrations publish posts to WordPress, Shopify, Wix, Webflow, Ghost, and more, where your schema plugin or template adds the JSON-LD. Here’s how to connect WordPress to Contentpen.
• See what AI engines say: AI visibility tracking shows whether ChatGPT, Perplexity, Gemini, and other engines mention your brand after you publish.

Find your brand in AI search

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Find the top sources driving citations

Try AI Visibility →

Want every new post to arrive structured, scored, and ready for markup? Try Contentpen on your next article.

Start with one snippet and build from there

You don’t need all 10 examples on day one. Start with Organization and WebSite on your homepage, then BreadcrumbList across the site.

After that, add the page-level type that fits each template, such as Article, Product, or LocalBusiness. Test each one before it goes live.

Small, accurate snippets beat sprawling ones every time. Copy, adapt, validate, and repeat.

Frequently asked questions

SEO (search engine optimization) helps your pages rank in Google’s results so people click through. GEO (generative engine optimization) helps your brand get mentioned and cited inside answers written by ChatGPT, Perplexity, Gemini, and Google AI Overviews.

One earns a position. The other earns a place in the answer itself.

Think about the last time you asked an AI tool for a recommendation. Did you scroll through ten blue links afterward, or did you trust the three names it gave you?

Your buyers do the same. SparkToro’s 2026 clickstream study found that 68% of US Google searches now end without a click. Strong rankings still matter, but they no longer guarantee you get seen.

The smart move isn’t to drop SEO for GEO. It’s to understand what each one rewards and run them as one strategy.

Below, you’ll see how they differ, how AI engines pick sources, which tactics carry over, and how to split your effort.

Key takeaways

• SEO wins rankings and clicks. GEO wins mentions, citations, and recommendations inside AI answers.
• SEO evaluates whole pages. GEO is judged passage by passage and across everything the web says about your brand.
• Ranking still helps you get cited, but the link is weakening as AI engines pull from deeper in the results.
• GEO is not a replacement for SEO. It’s an extra layer built on crawlable, trusted, well-structured content.
• Measure GEO with share of voice, citation rate, and sentiment alongside rankings and traffic.
• Most teams can run both with one content process and a small shift in time and priorities.

What does SEO mean in 2026?

SEO is the practice of improving your website so search engines rank it higher for relevant queries. The reward is a click from someone who chose your result over the others.

The core hasn’t changed much. You still need:

• Content that matches search intent
• A site Google can crawl, render, and index
• Backlinks and mentions that signal authority
• A page experience people actually enjoy

What has changed is the results page around you. AI Overviews, AI Mode, videos, and forums now compete for the same screen space. 

What does GEO mean, and where did it come from?

GEO is the practice of shaping your content and online presence so generative AI engines mention, quote and recommend you. Success isn’t a position on a page. It’s being part of the answer.

The term comes from a 2023 research paper on generative engine optimization by researchers at Princeton and partner institutions. They showed that content changes like adding statistics, quotes, and sources could lift a page’s visibility in AI responses by up to 40%.

So what does a generative engine actually do?

It reads many sources, writes one answer, and credits a handful of them. GEO is about being one of those few, and it sits inside the wider practice of AI search optimization.

GEO vs SEO: what’s the actual difference?

The biggest shift is what gets judged. SEO scores a page. GEO scores passages from your page plus everything else the web says about your brand.

One row deserves a second look: what the user sees. In SEO, you compete for attention on a list. In GEO, the AI has already made the shortlist for the user.

How do AI engines decide which sources to cite?

No AI company publishes a full formula. Still, most AI search tools follow a similar path when they answer a question.

1. They expand the question: One prompt becomes several related searches behind the scenes, often called query fan-out.
2. They retrieve candidate pages: Many pull from a live search index, so crawlable, indexed pages have the edge.
3. They pick useful passages: Short, self-contained answers with clear facts are easier to lift.
4. They write the answer: The model blends those passages with what it already knows about each brand.
5. They attribute sources: A few URLs get cited or linked, and some brands get named without a link.

This is where SEO and GEO start to drift apart.

In July 2025, Ahrefs found that about 76% of AI Overview citations came from pages ranking in Google’s top 10. By March 2026, Ahrefs’ updated study put that figure at roughly 38%.

In other words, ranking still helps.

But more citations now go to pages that rank lower or not at all, as long as they answer the sub-question well.

Is GEO replacing SEO?

No, and the reason is simple. Most AI engines still need the open web to answer fresh questions, and they reach it through search indexes.

If Google or Bing can’t crawl and trust your page, AI tools built on those indexes are less likely to find it. Google’s own guidance on generative AI features in Search frames this work as part of SEO, not a separate discipline.

What is changing is the payoff. A top ranking used to mean steady clicks. Now it often means being one input to an answer the user reads without visiting you.

What does SEO still do for GEO?

More than most GEO advice admits. A lot of the groundwork is shared.

• Crawlability and indexing: AI search tools can’t cite what they can’t fetch.
• Topical authority: A cluster of strong pages signals expertise to rankers and models alike.
• Backlinks and mentions: Quality SEO backlinks double as trust signals that models pick up.
• Clear on-page structure: Headings, short paragraphs, and lists help both crawlers and passage extraction.
• Freshness: Updated facts help you rank and stop AI from repeating stale details about you.

Know exactly what to fix, before publishing to get it ranked!

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If your site already passes a solid on-page SEO checklist, you’ve done much of the GEO prep without realizing it.

Where do AEO and AIO fit next to GEO and SEO?

You’ll see a few more acronyms in this space. Our SEO vs GEO vs AEO guide compares all three.

Here’s the short version of each full form.

• SEO (search engine optimization): ranking pages in search results.
• AEO (answer engine optimization): winning the single direct answer in snippets, voice, and AI Overviews.
• GEO (generative engine optimization): earning mentions and citations in AI-written answers.
• AIO (AI optimization): a loose umbrella term, sometimes used just for Google AI Overviews.

Think of SEO as the foundation, answer engine optimization as the bridge, and GEO as the newest layer. The labels overlap, so focus on the outcome you want rather than the acronym.

If you’re deciding between the two AI layers, read AEO vs GEO.

Which SEO tactics should you keep, and what should you add for GEO?

You don’t need a second playbook. You need a few additions to the one you have.

Notice the theme? SEO tactics mostly live on your site. GEO tactics push you outward to the places models learn from.

Each engine also behaves a little differently. Our comparison of how Perplexity and ChatGPT differ shows why it pays to test prompts on more than one.

How do you measure GEO compared with SEO?

SEO reporting is mature. GEO reporting is newer, so you’ll need a few fresh metrics next to your usual SEO analytics.

Run the same prompts every month. AI answers vary between runs, so trends tell you far more than one screenshot.

Find your brand in AI search

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How should you split time between SEO and GEO?

There’s no universal ratio, and anyone quoting one is guessing. Your split should follow where your buyers research.

• New site with little authority: put most effort into SEO basics. AI engines rarely cite brands nobody has heard of.
• Established site with steady rankings: keep SEO running and move a meaningful slice of time to GEO work like digital PR and prompt tracking.
• B2B or high-consideration products: lean further into GEO. Buyers often ask AI tools for shortlists before they ever search your brand.
• Local or transactional businesses: stay SEO-heavy, since many searches still end on maps, product pages, and booking forms.

Review the split each quarter. If your AI share of voice grows while rankings hold, you’ve found a balance that works.

What mistakes do teams make when moving from SEO to GEO?

A few patterns show up again and again.

1. Pausing SEO to fund GEO: You weaken the foundation AI engines rely on.
2. Chasing a single AI tool: Visibility in ChatGPT doesn’t guarantee visibility in Gemini or AI Overviews.
3. Publishing vague thought pieces: Models quote specifics, not opinions without evidence.
4. Ignoring what others say about you: Outdated reviews and old listicles shape AI answers too.
5. Expecting overnight results: Off-site mentions take time to build and get picked up.

How does Contentpen help you run SEO and GEO together?

Splitting SEO and GEO across separate tools makes it hard to see the full picture. Contentpen keeps both in one workflow, from research to reporting.

• Spot what to fix first. SEO opportunities surface competitor gaps, pages worth recovering and keyword cannibalization from your Search Console data.
• Write for rankings and answers. Articles come with key takeaways, FAQs and question-based headings that AI engines can lift easily.
• Check both scores live. SEO and GEO scoring grades each draft as you write, and “Fix with AI” resolves issues from both sides at once.
• Optimize for AI search. The AI search optimization tool helps shape content for how AI engines read and cite pages.
• Track where you show up. AI visibility tracking covers ChatGPT, Google AI, Perplexity, Gemini, Copilot, Grok, and Claude, with share of voice and source gaps.

You can publish straight to WordPress, Shopify, Wix, and other platforms when a piece is ready.

Want to rank on Google and get cited by AI from one dashboard? Start with Contentpen’s AI SEO tool today.

Rank for the click, earn the citation

SEO and GEO chase different rewards. SEO wins the visit, while GEO wins a spot in the answer before anyone visits.

They still share the same roots: crawlable pages, real expertise, clear structure, and a trusted brand. Keep your SEO strong, add GEO tactics on top, and measure both every month.

The brands that do this now will be the names AI keeps repeating later.

Frequently asked questions