llms.txt
What is llms.txt and why your site needs one for AI visibility
What is llms.txt?
llms.txt is a proposed standard file (placed at /llms.txt on your domain) that provides a structured, plain-text summary of your website specifically for large language models. While robots.txt controls whether AI crawlers can access your site, llms.txt helps them understand what your site is about, what content is most important, and how it's organized. Think of it as a "readme" for AI — a concise document that gives AI models the context they need to accurately represent your content in search results.
llms.txt is a plain-text file placed at the root of a website (/llms.txt) that provides large language models with a structured summary of the site's content, purpose, and key pages — acting as a curated guide that helps AI search engines accurately understand and cite the site in generated answers.
Why does llms.txt matter?
AI search engines synthesize information from many sources into a single answer. Without clear context about your site, AI models may misrepresent your content, miss your most important pages, or fail to cite you altogether. An llms.txt file gives AI systems a curated overview of your site — your mission, key pages, products, and expertise — increasing the likelihood that AI-generated answers accurately cite and reference your content. Early adopters report higher AI citation rates and more accurate brand representation in AI search results.
Key statistics
Sites with an llms.txt file saw a 15-25% increase in AI citation accuracy in early studies, with AI models more likely to correctly describe the site's offerings.
Source: llms-txt.org
Only an estimated 2% of websites currently have an llms.txt file, making it an early-mover advantage for AI search visibility.
Source: Web Almanac 2025
How to fix it
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1
Create a plain text file named llms.txt in your site's root directory (accessible at yourdomain.com/llms.txt).
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2
Start with a brief description of your organization and what your site offers — write it as if explaining to someone (or something) that has never visited your site.
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3
List your most important pages with their URLs and a one-sentence description of what each page covers.
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4
Include key topics, products, or services you want AI models to associate with your brand.
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Keep it concise (under 2,000 words) and update it whenever you add major new content or pages.
Code example
# Bad: No llms.txt file exists
# AI models must guess what your site is about
# based on whatever pages they happen to crawl.
# This leads to incomplete or inaccurate
# representation in AI search results.# llms.txt - Example Company
> Example Company helps small businesses
> build accessible, SEO-optimized websites.
## Key Pages
- /products: Our main product suite
- /pricing: Plans starting at $29/month
- /blog: SEO and accessibility guides
- /docs: Technical documentation
## Topics We Cover
- Web accessibility (WCAG 2.1)
- Technical SEO
- AI search optimization
- Site performanceFrequently asked questions
Related topics
Robots.txt for AI Search Engines
Robots.txt is a text file at the root of your website that tells web crawlers which pages they can and cannot access. With the rise of AI search engines, robots.txt has become the primary way to control whether AI crawlers like GPTBot (OpenAI), ClaudeBot (Anthropic), Google-Extended (Google), and PerplexityBot (Perplexity) can crawl your content. Each AI company has its own crawler user-agent, and you need specific directives for each one to control access.
Structured Data
Structured data is machine-readable markup (typically JSON-LD using the Schema.org vocabulary) embedded in your page's HTML that explicitly describes the content's type, properties, and relationships. It tells search engines and AI systems exactly what your content is — an article, a product, a recipe, an FAQ — rather than requiring them to infer it from unstructured text.
AI Content Patterns
AI content patterns are specific content structures that make pages easier for AI search engines to extract, understand, and cite. Key patterns include definition blocks (a concise 40-60 word summary paragraph following each H2), evidence sandwiches (claim → supporting data → source citation), and self-contained answers (paragraphs that fully answer a question without requiring surrounding context). These patterns align with how AI systems parse and select content for generated answers.
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