What is llms.txt?
Definition
llms.txt is a proposed Markdown file that helps language-model applications find useful website resources. It usually provides a concise site overview and curated links, but it is not an access-control standard or a guaranteed AI visibility signal.
How does llms.txt work?
The llms.txt proposal describes a Markdown file published at /llms.txt or within a site path, such as /docs/llms.txt, covering pages under that path.
It can include a heading, a short summary, explanatory context, and categorized links to useful resources. A compatible application can use that curated overview when working with the site.
The proposal is intended to make relevant information easier to locate, not to require every crawler or answer engine to read the file.
- A concise summary explains what the website provides.
- Linked sections point toward maintained and useful resources.
- Optional material can be separated from the most important references.
Why does llms.txt matter for B2B SaaS?
SaaS documentation can be difficult to navigate when API references, tutorials, and product explanations live across many paths. A curated resource index can help applications that explicitly support the proposal.
Its practical value depends on actual consumer behavior and maintenance. Publishers should not infer that adding the file changes model training permissions, improves rankings, or makes ChatGPT cite every listed page.
- Prioritize stable documentation and clear product reference pages.
- Keep the file synchronized with major documentation reorganizations.
- Treat adoption as an experiment rather than a mandatory ranking requirement.
What is an example of llms.txt?
An API platform could publish a short introduction followed by links to authentication guidance, SDK documentation, rate-limit explanations, and migration notes.
Each link can include a brief description so a consuming application can choose the relevant resource. This index should point to public information that is safe to access.
Private credentials and confidential customer configuration do not belong in a public convenience file.
- Use descriptive labels that distinguish tutorials from API references.
- Link to current canonical pages rather than obsolete redirect chains.
- Exclude sensitive information even when the file seems machine-oriented.
How do you improve or measure llms.txt?
Check that the file is publicly reachable and follows the proposal’s structure. Test every linked resource and inspect whether the descriptions still match the destination.
Server logs can reveal requests for the file, but a request alone does not establish that a downstream answer used it. Compare concrete integration behavior where possible. Keep crawler permissions and search accessibility under their appropriate controls.
- Track broken links and outdated resource descriptions during releases.
- Measure verified consumption separately from general website bot traffic.
- Avoid interpreting an unverified user-agent string as proof of adoption.
Frequently asked questions about llms.txt
Is llms.txt the same as robots.txt?
No. llms.txt proposes a resource-discovery format. robots.txt communicates crawl instructions to participating crawlers and serves a different purpose.
Does llms.txt guarantee inclusion in AI answers?
No. The proposal does not guarantee that an engine reads the file, retrieves linked pages, or cites them in a response.
Should a SaaS company publish llms.txt?
It can be useful when supported applications need a curated documentation index. First ensure the underlying pages are accessible, accurate, and worth maintaining.