llms.txt

Definition

llms.txt refers to a proposed metadata standard in the form of a text or Markdown file stored in the root directory of a website. It provides large language models (LLMs) such as ChatGPT, Google Gemini, or Claude with a curated overview of the most important and relevant content of a domain. Unlike robots.txt or sitemap.xml, it is specifically designed for access and processing by AI systems and does not list all pages, but only selected, AI-relevant resources.

llms.txt covers all structured measures that allow website operators to define which content is particularly relevant for LLMs, how it should be prioritized, and which areas are excluded from use by AI systems. The goal is to make it easier for generative AI applications to identify and evaluate important content, maintain control over usage, and take data protection as well as copyright aspects into account.

Target Groups

  • Companies and organizations that want to provide high-quality content for AI systems or control its use
  • Operators of news portals, specialist information sites, blogs, and knowledge databases
  • AI providers seeking to work with web content responsibly and transparently

Benefits

  • Relevance: LLMs access selected, verified content
  • Control: Determine which data may be processed by AI systems
  • Protection: Exclude sensitive or copyright-protected areas
  • Transparency: Clear communication of usage conditions for AI providers
  • Efficiency: Faster and more targeted processing by AI systems

Key Components

  • Structured link lists to prioritized content
  • Short descriptions and categorization
  • Allow/Disallow instructions for specific agents
  • Prioritization details
  • Markdown-based formatting

Priorities

Relevance and quality of the listed content, exclusion of non-approved data, clear rules for AI providers, simple implementation, and machine readability.

Trends

  • Growing importance in the context of AI content usage
  • Increasing demand for transparency standards for generative models
  • Potential integration into future web standards or regulatory frameworks