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Cerbos - All Posts

Authentik vs Keycloak: Self-hosted IdP comparison Mapping business requirements to authorization policy for automotive Fine-grained authorization for AI gateways EIC 2026: Stop counting agents, protect what they can touch Agent skill for writing authorization policies in Claude Desktop Identity security in 2026 EIC 2026 takeaways: the identity stack built for humans will not hold up for AI agents Already have authentication? Here's the authorization layer you still need. Tokens are authorization decisions: a guide to policy-driven token issuance What is a Runtime Authorization Platform It's a dimmer switch, not a kill switch. How CISOs are rethinking AI agent governance From maps to bitmaps (and from bitmaps to bitmaps) AuthZEN, Shared Signals, SCIM Events, IPSIE: Notes from the OpenID Enterprise Panel How do you update authorization policies without redeploying your application? IIW42 recap: Where agent authorization got real Cerbos PDP v0.52.0/v0.53.0: Engine performance, security hardening, and CEL path functions Authorization Management Platforms: what they do, how they work, and where they fit PocketOS AI coding agent deleted a production database in 9 seconds Non-Human Identity management still has a blind spot Supabase alternative in 2026: Best open source auth options Benefits of on-premise authorization: Why enterprises are moving toward self-hosted Authorization policies: How to write, test, and validate them (faster with AI) Agent skill for writing authorization policies How much does it cost to build authorization in-house? Why centralized authorization governance reduces incident response time OPA alternative Why AI agents make authorization a right now problem Modernizing legacy application authorization: why it’s your biggest security blind spot How to add authorization to legacy applications without code changes 5 authorization blind spots auditors find, and how to fix them
Supercharging LLM understanding of Cerbos documentation
Alex Olivier · 2025-07-15 · via Cerbos - All Posts

Large Language Models (LLMs) are becoming an integral part of how we interact with information and the web. As they evolve, their ability to understand and process vast amounts of online content is improving in near real-time. However, this process isn't without its challenges.

Modern websites are often cluttered with content that, while useful for human interaction, can be confusing for an LLM. Ads, navigation menus, pop-up banners, and scripts can obscure the core information on a page. This means that when you ask an LLM a question, it might miss key details buried within a website's complex structure.

To address this, a new standard is emerging: the llms.txt file. We at Cerbos have embraced this to ensure our documentation is as clear and accessible to these models as it is to our users.

What is llms.txt?

At its core, llms.txt is a simple yet powerful idea. It's a Markdown file, located at the root of a website (e.g., https://docs.cerbos.dev/llms.txt), that provides a clean, concise, and LLM-friendly version of the site's content. It acts as a set of notes, giving LLMs the essential information they need to understand what a site or a specific page is about, free from the noise of modern web design.

The primary benefits of this approach are:

  • Efficiency: By providing a distilled version of the content, LLMs can generate better and more accurate responses, even if their training data didn't include the specific content.
  • Improved context: LLMs have a limited "context window," meaning they can only process so much information at once. Feeding them only the most relevant content maximizes their ability to understand the subject matter.
  • Better user experience: When LLMs have a clearer understanding of our documentation, they can more accurately match a user's query to the solutions Cerbos provides, leading to a better experience for everyone.

The proposed standard also includes an extended version of this file, llm-full.txt. This file, which we have also implemented, can contain more detailed information, such as specific URLs and additional sections, for a more comprehensive overview.

Why we added it to the Cerbos documentation

Just as with traditional SEO, our goal is to ensure that the information people find about Cerbos is accurate, helpful, and reflects our messaging. We want to guide LLMs to the most important features and documentation, especially as our product evolves and documentation is updated.

By providing llms.txt and llms-full.txt files for our documentation, we can help these models navigate the content, distinguish between different versions, and surface the most relevant information for our users' queries.

Open sourcing our solution for the Antora community

The Cerbos documentation is built using Antora, a popular open-source documentation site generator. To implement llm.txt, we developed our own Antora extension to automatically generate these files from our source content.

In the spirit of open source, we are delighted to share this tool with the wider community. We have open-sourced the Cerbos Antora LLM Generator, making it easy for any project using Antora to add llm.txt and llm-full.txt files to their documentation sites.

We invite you to take a look at the generated files on our documentation site:

As the way we find and consume information continues to evolve, we must adapt. By embracing new standards like llm.txt and contributing to the open-source tools that power them, we are helping to build a more accessible and intelligent web for everyone.