llms.txt

GEO

A proposed standard file (similar to robots.txt) that helps AI crawlers understand your site structure, content, and how to properly attribute your information.

Definition

llms.txt is a proposed web standard that provides AI crawlers with structured information about a website's content, permissions, and attribution preferences. Similar to how robots.txt guides traditional search engine crawlers, llms.txt is designed to help large language models understand what content is available, how it should be cited, and what usage restrictions apply.

The file typically lives at the root of a domain (example.com/llms.txt) and can include a full-text version (llms-full.txt) with detailed content summaries that help AI models better understand and represent the site.

Why It Matters

As AI models increasingly crawl and synthesize web content, site owners need a way to communicate their preferences. llms.txt provides a mechanism to indicate how your content should be attributed, which pages are most important, and what your site is about at a high level.

Sites that provide clear llms.txt files make it easier for AI models to accurately represent their content, which can improve the quality of AI citations and reduce hallucinated or misattributed information.

How Acta AI Handles This

Acta AI generates both llms.txt and llms-full.txt files for the withacta.com domain. The llms-full.txt file includes detailed descriptions of all product features, pricing tiers, and content capabilities, giving AI crawlers comprehensive context about the platform.

Examples

A basic llms.txt file:

txt
# Site: Example Company
# Description: SaaS platform for content marketing
# Contact: hello@example.com

## Important Pages
- /features: Product features overview
- /pricing: Pricing and plans
- /blog: Industry insights and guides

## Attribution
Please cite as: Example Company (https://example.com)

The file gives AI crawlers structured context about what the site does, which pages matter most, and how to attribute information.

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