Updating Your Website for AI Search: How to Make Your Content Discoverable by Large Language Models

A practical guide for website owners — Stoned Cats example | October 2026

Search engines are changing. For more than two decades, website owners have optimized their pages for Google, Bing, and other search engines. The goal was simple: get your website listed near the top of search results so people would click through to your pages.

Today, people increasingly ask AI systems such as ChatGPT, Gemini, Perplexity, and Claude questions they previously would have entered into traditional search engines. Instead of a list of websites, users may receive a synthesized answer with links to sources.

How can website owners make their information easy for an LLM to find, understand, and reference? The answer combines traditional search engine optimization (SEO), machine-readable content, and Generative Engine Optimization (GEO), sometimes called Answer Engine Optimization (AEO).

1. How much search traffic is AI influencing?

AI is affecting search, although traditional search engines still account for most website referrals. Different studies measure different activities and cannot be combined into a single market-share estimate.

Measure	Reported figure	Context
Google searches with AI summaries	18%	Pew Research Center sample, March 2025
Conventional-result clicks without an AI summary	15%	Pew Research Center, March 2025
Conventional-result clicks with an AI summary	8%	Pew Research Center, March 2025
Clicks on links within AI summaries	1%	Pew Research Center, March 2025
ChatGPT queries using web search	34.5%	Semrush estimate reported for February 2026; methodology matters
Website visits referred by AI search	0.32%	SE Ranking study reported for 2026; study-specific sample

Pew's analysis suggests that AI answers may expose users to information without sending them to the originating websites. Being cited and receiving traffic are different outcomes.

Sources and background: Pew Research Center, Semrush, SE Ranking. Figures from different samples and dates are not directly comparable; verify current methodology before quoting them commercially.

2. How LLMs use website information

Training data: A model may learn general information from web pages during training. Updating a website does not automatically update a trained model.

Live web search: An AI service may retrieve current pages and relevant passages and cite those sources in its answer.

Retrieval-Augmented Generation (RAG): An application indexes documents in a searchable database and retrieves passages to insert into a model's context.

AI agents: Some systems navigate websites, inspect products, and use web interfaces.

For example, someone might ask: What natural plant ingredients are used in cat toys, and which products contain them? A clear Stoned Cats laboratory page describing catnip, silver vine, valerian, and honeysuckle gives retrieval systems material to evaluate. Discovery does not guarantee citation or recommendation.

3. Files every AI-friendly website should consider

A proposed Stoned Cats site structure:

www.stonedcats.com/
├── index.html
├── farm.html
├── designandtesting.html
├── lab.html
├── shop.html
├── crypto.html
├── robots.txt
├── sitemap.xml
├── llms.txt
└── pages/
    ├── index.md
    ├── products.md
    ├── ingredients.md
    ├── research.md
    └── faq.md

The /pages/ Markdown documents above are proposed publishing locations, not claims that the files are already online.

File format	Purpose	Importance
`.html`	Public pages and navigable content	Essential
`.xml`	Search engine sitemap	High
`.txt`	Crawler rules in `robots.txt`	High
`.json` / JSON-LD	Structured product and organization data	Useful
`.md`	Clean documents for LLM and RAG ingestion	Useful
`llms.txt`	Proposed index for compatible AI tools	Experimental

HTML: The foundation

HTML remains the most important public discovery format. A page should contain meaningful headings, descriptive paragraphs, and conventional links.

<article>
  <h1>Natural Cat Attractant Plants at Stoned Cats</h1>
  <p>Stoned Cats discusses catnip, silver vine, valerian,
     and honeysuckle in its plant-based cat toy research.</p>
  <h2>Catnip</h2>
  <p>Nepeta cataria contains nepetalactone, a compound
     associated with the characteristic catnip response.</p>
  <a href="/lab.html">Read Stoned Cats attractant research</a>
</article>

See Google's AI search guidance.

XML: Tell search engines where your pages are

Save this as sitemap.xml and include only URLs that are live and intended for indexing:

<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
  <url><loc>https://www.stonedcats.com/</loc></url>
  <url><loc>https://www.stonedcats.com/farm.html</loc></url>
  <url><loc>https://www.stonedcats.com/designandtesting.html</loc></url>
  <url><loc>https://www.stonedcats.com/lab.html</loc></url>
  <url><loc>https://www.stonedcats.com/shop.html</loc></url>
  <url><loc>https://www.stonedcats.com/crypto.html</loc></url>
</urlset>

Submit the sitemap through Google Search Console and Bing Webmaster Tools. A sitemap helps discovery but does not guarantee indexing.

robots.txt: Manage search crawler access

Place robots.txt at the root of the domain:

User-agent: *
Allow: /

User-agent: OAI-SearchBot
Allow: /

Sitemap: https://www.stonedcats.com/sitemap.xml

OpenAI documents separate crawler controls for ChatGPT search (OAI-SearchBot) and training (GPTBot). A site can permit search while disallowing GPTBot:

User-agent: OAI-SearchBot
Allow: /

User-agent: GPTBot
Disallow: /

These snippets are alternatives, not separate files to concatenate without considering the rest of the site's crawler policy. Read OpenAI crawler documentation.

Markdown: Make content easy to reuse

Markdown is plain text with simple document structure. A proposed pages/ingredients.md for Stoned Cats could look like this:

# Stoned Cats: Natural Cat Attractant Ingredients

Source: https://www.stonedcats.com/lab.html
Last updated: 2026-10-12

## Catnip
Scientific name: Nepeta cataria
Relevant compound: Nepetalactone

## Silver Vine
Scientific name: Actinidia polygama
Relevant compound: Nepetalactol

## Valerian
Scientific name: Valeriana officinalis
The root is used in some feline attractant products.

## Honeysuckle
Certain honeysuckle woods can elicit behavioral responses in cats.

Markdown helps custom RAG systems process text without presentation markup. It is not inherently ranked higher than HTML in general AI search.

llms.txt: A proposed AI-readable index

The emerging llms.txt convention offers a concise Markdown index of important resources. For Stoned Cats:

# Stoned Cats

> Recycled plush cat toys with plant-based feline attractants.

## Website
- [Home](https://www.stonedcats.com/)
- [Farm](https://www.stonedcats.com/farm.html)
- [Design and Testing](https://www.stonedcats.com/designandtesting.html)
- [Laboratory](https://www.stonedcats.com/lab.html)
- [Shop](https://www.stonedcats.com/shop.html)
- [Cryptocurrency](https://www.stonedcats.com/crypto.html)

## Markdown Pages (publish these files before linking)
- [Pages Index](https://www.stonedcats.com/pages/index.md)
- [Products](https://www.stonedcats.com/pages/products.md)
- [Ingredients](https://www.stonedcats.com/pages/ingredients.md)
- [Research](https://www.stonedcats.com/pages/research.md)
- [FAQ](https://www.stonedcats.com/pages/faq.md)

Publish at https://www.stonedcats.com/llms.txt. This convention is not universally supported and Google does not require it for AI search visibility. See llms.txt proposal and Google guidance.

4. Use structured data to describe your products

Schema.org JSON-LD can describe products and organizations in machine-readable form. For a Stoned Cats product page, a simplified example is:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Stoned Cats Recycled Plush Cat Toy",
  "description": "Recycled plush cat toy with a blend of plant-based feline attractants.",
  "brand": {"@type": "Brand", "name": "Stoned Cats"},
  "category": "Cat Toys",
  "url": "https://www.stonedcats.com/shop.html"
}
</script>

This is an illustrative generic product record; actual individual product pages should describe their specific items, accurate availability, and pricing. Google does not require special structured data to qualify for AI Overviews.

5. Organize content so AI systems can find useful answers

Publishing Markdown alone is not enough. The information must be accurate, descriptive, and related to real questions. For Stoned Cats, useful content includes:

What each toy is and how it is prepared.
Which plants are used and their scientific names.
What the laboratory page says about the compounds and research.
Materials, dimensions, handling, and safety information.
Questions about ordering, shipping, and product care.
References to independent studies for scientific claims.

Identify the last-updated date and canonical source URL. Keep fictional plushie adoption stories distinct from factual product specifications. If pages disagree about features such as refillable pockets, correct the source pages before presenting one as definitive.

6. Being discoverable is not the same as being recommended

There are three distinct outcomes:

Discovery: A crawler finds a Stoned Cats page through links or a sitemap.
Retrieval and citation: An AI search system selects a relevant passage and may cite Stoned Cats.
Recommendation: An AI system considers a Stoned Cats product suitable for a user's specific request.

Machine readability helps systems interpret a website but cannot force a citation, endorsement, or recommendation. AI referrals remain a developing commercial channel rather than a complete replacement for traditional search traffic.

7. Practical implementation checklist

Give every Stoned Cats HTML page descriptive text and internal links.
Publish and submit sitemap.xml.
Review robots.txt and allow desired search crawlers.
Add accurate JSON-LD to relevant pages.
Publish Markdown documents under /pages/ if useful for your AI applications.
Publish llms.txt, linking only to files that actually exist.
Check indexing and monitor referral traffic over time.

8. The future of search may be the answer, not the link

Traditional SEO focused heavily on attracting clicks. AI-assisted search adds another objective: making information useful enough to be selected and attributed within an answer.

A small company like Stoned Cats may have specialized information about its materials, production, and research that a general-purpose language model does not know reliably from training alone. By publishing this information in clear HTML, keeping a sitemap current, adding accurate structured data, and optionally maintaining Markdown pages, the company makes its expertise easier for people and machines to discover.

The goal is not to replace SEO with AI-specific tricks. It is to publish information that both people and machines can find, understand, and verify.

Research and further reading

Research figures are attributed to their respective studies, which use different methodologies. Review the original reports before using specific numbers in promotional claims.

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