AI assistants have changed how people discover and use documentation.

Rather than scanning a sidebar and skimming headings, now they ask an LLM for answers — and expect it to cite, summarize, and explain your content for them.

So what does that mean for your docs? For starters, you need them to be reliable and AI-ready. But you also need a way to tell these models what to ingest. And that’s where llms.txt comes in.

In this post, we’ll explain what llms.txt is, why it matters for AI ingestion of documentation, and how to implement it — including a quick step-by-step guide, and a look at llms-full.txt.

What is llms.txt?

llms.txt is an emerging convention that signals to AI crawlers which parts of your site are intended for LLM ingestion. You can think of it like your site’s robots.txt, but designed for LLMs: it points to canonical documentation, structured exports, sitemaps, and ingestion-friendly resources.

Why is that useful? Well, it creates AI-friendly hints so LLMs can prioritize authoritative, up-to-date sources. This improves answer quality in AI assistants and reduces the risk of outdated or duplicate pages getting ingested.

What does an llms.txt file look like?

Here’s a quick example of a simple llms.txt file for a documentation site:

# Product Docs example

## Documentation
- [Welcome](/docs/getting-started/welcome.md): Introduction to the product
- [Quickstart](/docs/getting-started/quickstart.md): Get up and running with the product in minutes

## Guides
- [Guides](/docs/guides/readme.md): Explore features in more detail, discover new ways to work, and learn best practices
- [Install](/docs/guides/installation.md): Learn how to get set up with the product

The standard is still very new, so there isn’t a single, universal llms.txt format yet, but this example shows common fields an llms.txt file might include:

• An H1 title

• H2 sections with bulleted lists showing the content of each section

• Formatting to help guide an LLM to the Markdown-formatted content of each page

• Details about how the site is organized and how to interpret files

• Optional metadata about the site that can be skipped by the LLM if needed

Why llms.txt matters

Your documentation doesn’t just answer questions for your users — it also influences purchasing decisions for prospects.

Making sure that LLMs find the right pages helps your product show up accurately when users ask AI for product support, or prospects research your product.

For technical writers and engineers, llms.txt delivers several benefits:

• Control and clarity – Direct crawlers toward canonical sections and away from outdated or experimental pages.

• Faster, higher-quality ingestion – Well-structured formats reduce scraping overhead and improve recall.

• Better support experiences – If your support bot or AI assistant relies on public docs, llms.txt helps it stay in sync with releases.

From an SEO perspective, llms.txt complements sitemaps and metadata — but doesn’t replace them. It adds a clear signal to LLMs that says “these are the sources of truth,” which can help optimize docs for LLMs while keeping your traditional search posture intact.

The good news is, you don’t have to do all of this work yourself. Some documentation platforms are meeting you halfway, by automatically generating llms.txt for you.

For example, GitBook automatically creates an llms.txt file for your docs site as soon as you hit Publish, so your docs are prepared for AI crawlers without extra maintenance.

While llms.txt is gaining traction as a standard, it's worth noting that its effectiveness is still being evaluated.

Some developers question whether AI crawlers consistently honor these files, and there's ongoing discussion about whether the benefits outweigh the maintenance overhead for smaller sites.

However, given the low implementation cost and potential upside, many organizations are adopting it as a best practice.

Use cases

Creating llms.txt is typically a good idea for all modern public websites — documentation or otherwise. But here are some specific use-cases for different job roles.

Engineers maintaining API docs

If you publish a stable REST or GraphQL reference, llms.txt can point crawlers to canonical endpoints, versioned paths, and a Markdown export. That helps LLMs answer questions using precise parameter definitions and examples, rather than scraping ad hoc blog posts or forum threads.

Technical writers managing product guides

Product guides are constantly changing. So directing LLMs to your latest release space, while excluding archived sections, keeps AI answers current.

Product teams using docs for onboarding

If your onboarding experience includes in-app hints or an AI assistant, pointing LLMs at your “Getting Started” and “How-to” hubs can improve first-week activation. You could also add a policy to clarify licensing and acceptable use for your content.

How to create llms.txt

Depending on your setup, there are a couple of ways to implement an llms.txt file for your docs site.

If you use a software documentation platform like GitBook, it will automatically generate and update llms.txt and related files, reducing overhead and keeping signals consistent as your docs evolve.

Alternatively, if you want to create the file manually, the exact steps depend on your hosting setup. That said, the high-level process is consistent.

1. Draft the file
   Create a plain text file named llms.txt at the root of your docs domain. Start with an H1 heading describing the site, add a description as a quote block if desired, then add your content below.
   
   

2. Host at the root
   Upload llms.txt at the root directory of your site, or on a relevant subpath, such as /docs/llms.txt. Most crawlers will look here first. Check that it’s publicly accessible with curl:

   curl -I https://docs.example.com/llms.txt
   
   

3. Keep it updated
   Update llms.txt when you launch new major versions, move sections, or add better ingestion endpoints (like a richer Markdown export). You might want to consider automation in your release pipeline, to streamline this process.

Want to get a head start with creating your file? Tools like this one can generate basic llms.txt files for any URL, which you can then build out to add more structure and data.

Beyond llms.txt: llms-full.txt and Markdown exports

What is llms-full.txt and why is it useful?

So we know that llms.txt is a concise guide. But in some setups, you can also provide llms-full.txt, which expands on llms.txt with a more exhaustive list of ingest-worthy pages and their content.

Where llms.txt points to sources, llms-full.txt contains the entire content of a website’s documentation in a single Markdown file. Which means it functions as a single ingestion point for all of the content of your docs.

Why are .md Markdown files useful for LLMs?

Some platforms also generate ingestion-ready .md files for every page. Markdown is friendly to both humans and machines, so LLMs can parse content without having to parse potentially complex HTML, improving accuracy.

If your docs stack supports exporting Markdown alongside HTML, include that export in your llms.txt file — like in the example file above.

Documentation tools like GitBook can help here, too. GitBook produces llms.txt and llms-full.txt for published docs. It also makes Markdown versions of every docs page accessible automatically, aligning with AI-optimized documentation practices without requiring custom scripts.

Combined with built-in sitemaps, variants, and access controls, you have all the tools you need to optimize your docs for LLMs with minimal setup. [link suggestion: GitBook AI-ready docs overview]

Should I create llms.txt?

If you're wondering "should I create llms.txt?" the answer is yes — especially if customers rely on AI assistants or you want to improve your visibility in AI tools.

While the jury is still out on how consistently AI crawlers respect these files, the implementation effort is minimal and you're future-proofing your documentation as the standard evolves.

The potential benefits — improved discoverability, simplifying the sharing of docs with AI assistants, and less risk of stale answers circulating — make it worthwhile.

Build your docs better with GitBook

Making AI-ready docs doesn’t have to be a separate project from the docs themselves.

With GitBook, everything you need for documentation AI ingestion is automatically generated and kept in sync with your content. Plus, you get features that make it easier for your customers to use your content in this way — such as an “Open in AI tool” button to help them bring a page into tools like ChatGPT or Claude as context.

If you want to find out why teams like Nvidia, Zoom, and Cortex use GitBook to optimize their docs for LLMs without extra steps, sign up and take it for a spin.