The idea behind LLMs.txt
An LLMs.txt file is a plain text file you place at the root of your website, specifically intended to help large language models (LLMs) understand your site. Think of it as a stripped-back, human-readable guide that tells AI systems: here is what this website is about, here are the most important pages, and here is how to interpret the content you find here.
The concept was proposed by Jeremy Howard in late 2024 as a direct response to a growing problem. AI systems crawl the web constantly, but they often have to make sense of pages loaded with navigation menus, cookie banners, JavaScript-heavy layouts, and other HTML noise that gets in the way of understanding what actually matters. LLMs.txt cuts through all of that by offering a clean, structured summary in Markdown format.
The format is deliberately simple. A typical LLMs.txt file includes a short description of what the business does, a list of key URLs with brief explanations of what each page covers, and any other context you want an AI model to have before it starts crawling. No schema syntax, no JSON. Just clear, structured text.
How it differs from robots.txt and sitemaps
People often ask whether LLMs.txt replaces robots.txt or sitemaps. It does not. These are three distinct tools that serve different purposes.
Your robots.txt file tells crawlers what they are and are not allowed to access. It is about permission. Your sitemap tells crawlers where all your pages are. It is about discovery. LLMs.txt is about interpretation. It does not restrict or index anything. It simply provides context so that when an AI model reads your site, it has a better chance of understanding what you actually do.
A useful analogy: robots.txt is the bouncer on the door, your sitemap is the floor plan, and LLMs.txt is the welcome card on the front desk that explains what kind of business this is and which rooms matter most.
Some sites also create a companion file called LLMs-full.txt, which includes the full text of key pages rather than just links and descriptions. This is particularly useful for AI systems that want to ingest your content without having to crawl multiple pages individually.
What an LLMs.txt file actually looks like
Here is a simple example to make this concrete. A software company selling project management tools might write something like this:
# Acme Project Tools
Acme helps small teams manage projects, deadlines, and client communications from one dashboard.
## Key pages
- [Home](https://acme.com/): Overview of the product and main features
- [Pricing](https://acme.com/pricing): Subscription tiers and feature comparison
- [Blog](https://acme.com/blog): Guides on project management and productivity
- [About](https://acme.com/about): Company background and team
That is the core structure. You can add more detail, group pages by category, include a brief description of your target audience, or note anything that would help an AI system avoid common misinterpretations. Some brands use it to clarify the difference between similar product names, or to signal which pages reflect their most up-to-date thinking.
The file lives at yourdomain.com/llms.txt and should be publicly accessible without authentication. It should also be updated when your site changes significantly, just like you would update your sitemap.
Is it an official standard?
Not yet, and that distinction matters. LLMs.txt is a community-driven proposal, not an accepted standard backed by Google, OpenAI, Anthropic, or any other major AI provider. There is no confirmed evidence that ChatGPT, Perplexity, Gemini, or Claude currently read LLMs.txt files during their crawling or response generation processes.
That said, the proposal has gained real traction. A growing number of well-known websites have already added LLMs.txt files, including some developer tools, SaaS products, and documentation sites. The open-source community has built parsers and validators for it. And the underlying problem it is trying to solve, helping AI systems extract meaning from cluttered web pages, is a genuine one.
The situation is not unlike the early days of Open Graph tags. For a while, they were an informal convention before platforms officially adopted them. LLMs.txt may follow a similar path, or it may not. Right now, adding one is a low-cost bet on a plausible future.
Do you actually need one?
This depends on what kind of site you have and how much you care about AI visibility right now.
Sites most likely to benefit
If your site has a lot of content, pages that are easy to misinterpret without context, or a brand name that could be confused with something else, an LLMs.txt file gives AI models a useful anchor. Documentation sites, SaaS products, e-commerce stores with large catalogues, and content-heavy blogs are good candidates.
If you are running a Shopify store, for example, your product pages, collection pages, blog posts, and policy pages all have very different purposes. An LLMs.txt file lets you explain that structure in plain language, something that schema markup and sitemaps do not quite capture in the same way.
Sites where it matters less
If your site is a simple three-page brochure site with a very clear niche, an LLMs.txt file probably adds minimal value right now. AI crawlers are reasonably good at understanding small, well-structured sites. Your time is better spent on structured data and high-quality content first.
The honest answer is that for most sites, LLMs.txt is not a replacement for proper schema markup, well-structured pages, and content written to be genuinely useful. It is a supplement, not a foundation. If you have not yet sorted out your how your content is written for AI engines, that is a better place to start.
LLMs.txt versus schema markup: which matters more?
Schema markup and LLMs.txt are solving related but different problems. Schema markup uses structured vocabulary from Schema.org to label specific pieces of information on your pages. It tells a machine: this string of text is a product name, this number is a price, this date is a review date. It operates at the data level.
LLMs.txt operates at the site level. It provides high-level orientation rather than granular labelling. The two genuinely complement each other, and the most AI-ready sites will likely end up using both.
If you had to prioritise, schema markup has a longer track record and is more directly tied to how search engines and AI systems surface specific answers. It also has measurable effects on rich results and citation likelihood. LLMs.txt is more speculative at this stage.
At FlinnSchema, the approach we take is to prioritise the structured data layer first because that is where the evidence is strongest, and then layer on additional signals like LLMs.txt as part of a broader AI readiness strategy. You can see how this fits into the wider picture on our what we do differently page.
How to create an LLMs.txt file in under 30 minutes
Creating a basic LLMs.txt file is straightforward. Here is a practical process:
Step 1: Open a plain text editor or a Markdown editor. Do not use Word or Google Docs without exporting as plain text.
Step 2: Write a one or two sentence description of your business at the top, introduced with a single hash as a heading.
Step 3: List your most important pages using Markdown link syntax, with a brief description of what each page contains. Aim for 10 to 20 pages. Do not try to list everything.
Step 4: Add any optional context that would help an AI system understand your site better. This might include your target audience, your primary products or services, or a clarification about your brand name.
Step 5: Save the file as llms.txt and upload it to the root of your domain. Test that it is accessible at yourdomain.com/llms.txt in a browser.
Step 6: Optionally create a second file called llms-full.txt that includes the full text of your most important pages, particularly any that are hard to crawl due to JavaScript rendering.
That is genuinely it. The barrier to entry here is low. The main effort is deciding which pages matter most and writing clear, honest descriptions of what each one contains.
Keeping your LLMs.txt file useful over time
One mistake people make with LLMs.txt is treating it as a one-time task. If your site evolves significantly, your LLMs.txt file should too. A file that points to outdated pages or describes an old version of your product can actively mislead AI systems.
Build a simple habit: whenever you add a major new section to your site, update your LLMs.txt file. If you run a content-heavy site, consider reviewing it quarterly. It only takes a few minutes and keeps the file genuinely accurate.
It is also worth cross-referencing your LLMs.txt with your understanding of how AI crawlers find your site more broadly. If certain pages are being blocked by your robots.txt file, listing them in LLMs.txt will not make them accessible. Make sure the two files are consistent.
For a broader view of your AI visibility across structured data, content, and technical signals, our free AI visibility audit is a good starting point to understand where the gaps are.
Frequently Asked Questions
Does Google read LLMs.txt files?
There is no official confirmation from Google that Googlebot or any Google AI system reads LLMs.txt files. Google has its own mechanisms for understanding site structure, including sitemaps, structured data, and internal linking. That said, the file does no harm and may be picked up by other AI crawlers that are more explicitly designed to look for it.
Is LLMs.txt the same as a sitemap?
No. A sitemap is a structured XML or HTML file that lists your pages for discovery and indexing purposes. LLMs.txt is a plain text or Markdown file designed to give AI models contextual understanding of your site. They work at different levels and for different purposes. You need both.
Will adding an LLMs.txt file improve my rankings?
Not directly, and not in any way that is measurable right now. LLMs.txt is not a ranking signal in traditional search. Its potential value lies in helping AI language models understand and represent your content more accurately when generating answers. Think of it as a long-term investment in AI readability rather than a quick ranking win.
What happens if my LLMs.txt file contains errors or outdated information?
Unlike schema markup, there is no formal validator for LLMs.txt, so errors will not generate penalties. However, pointing AI systems to broken URLs or describing your business inaccurately could lead to worse AI-generated representations of your brand. Keep it accurate and up to date for best results.