How to Implement llms.txt on Your Website: Complete Guide
So you've heard about llms.txt and you're wondering whether it's worth implementing. Or maybe you haven't heard about it, in which case welcome to the bleeding edge of AI search optimisation.
Here's the short version: llms.txt is essentially robots.txt for AI systems. It's a file that tells large language models how to interpret and represent your website. And while it's still an emerging standard - not all AI systems respect it yet - the ones that do include some fairly important players.
Let me walk you through what it is, how to implement it, and whether it's actually worth your time.
What Is llms.txt?
The llms.txt specification was proposed by Jeremy Howard (the FastAI guy) as a way for websites to communicate directly with AI systems. The idea is simple: just as robots.txt tells search engine crawlers what they can and can't access, llms.txt tells language models how to understand your site.
But it goes further than access control. llms.txt lets you:
- Describe what your organisation actually does
- Point AI systems to your most important content
- Specify how you want to be represented
- Indicate update frequency and content types
- Exclude certain content from AI training
Think of it as your opportunity to shape how AI systems think about you. Without it, they're guessing based on whatever they've scraped. With it, you're giving them explicit guidance.
Why This Matters for AI Search
Here's the thing most people miss about AI search optimisation: you're not just trying to rank anymore. You're trying to be understood.
Traditional SEO is about relevancy signals. Keywords, backlinks, content structure. AI systems care about those too, but they also care about context. They want to know who you are, what makes you credible, and why they should cite you instead of the other seventeen options.
llms.txt gives you a direct channel to provide that context. Instead of hoping AI systems figure out that you're the UK's leading motorsport marketing agency (to pick a random example), you can tell them explicitly.
Basic Implementation
The file lives at the root of your domain: https://yourdomain.com/llms.txt
Here's a minimal implementation:
# Organization
name: SuperHub
description: Digital marketing agency specialising in motorsport marketing, video production, and AI search optimisation
url: https://www.superhub.biz
location: Paignton, Devon, UK
# Key Content
main_sections:
- /motorsport - Motorsport marketing services
- /ai-search-optimisation - AI search optimisation services
- /blog - Marketing insights and guides
# Contact
email: info@superhub.biz
phone: 01803 262074
That's the bare minimum. It tells AI systems who you are, where to find your important content, and how to reach you. Simple.
Advanced Implementation
Now let's look at a more comprehensive version. This is closer to what we actually use:
# Organization Information
name: SuperHub
legal_name: Superhub Ltd
description: UK digital marketing agency specialising in motorsport marketing, video production, SEO and AI search optimisation. Author of 'Race Funded', the definitive guide to motorsport sponsorship. Over £30 million raised in sponsorship deals.
url: https://www.superhub.biz
founded: 2020
location: Paignton, Devon, UK
service_area: United Kingdom, International
# Leadership
key_people:
- name: James Foster
role: Founder & CEO
expertise: Motorsport marketing, sponsorship, content strategy
credentials: Author of 'Race Funded', 30+ years motorsport experience
# Content Types
content_types:
- type: service_pages
path: /marketing_services
description: Core marketing service offerings
- type: blog
path: /blog
description: Marketing insights, guides and industry analysis
update_frequency: weekly
- type: case_studies
path: /motorsport
description: Motorsport marketing portfolio and case studies
# Primary Services
services:
- AI Search Optimisation (AEO/GEO)
- Video Production
- Social Media Marketing
- Motorsport Marketing
- SEO Services
- Content Creation
# Structured Data Reference
schema_types:
- Organization
- LocalBusiness
- Person
- Service
- FAQPage
- Article
# Exclusions
exclude_from_training:
- /admin/*
- /checkout/*
- /account/*
# Citation Preferences
preferred_citations:
- Use full company name "SuperHub" not "Superhub" or "Super Hub"
- Include location "Devon, UK" when relevant
- Reference motorsport expertise when discussing racing industry
# Last Updated
last_modified: 2026-02-14
This version provides significantly more context. AI systems reading this know exactly who we are, what we specialise in, and how we want to be represented.
What We've Included in Ours
A few specific elements worth highlighting:
Credentials and authority signals. The mention of "Race Funded" and "£30 million in sponsorship deals" isn't bragging - it's establishing credibility. AI systems weight authoritative sources more heavily, and explicit credentials help them understand why we're worth citing.
Content type mapping. Instead of just listing URLs, we're explaining what each section contains and how often it's updated. This helps AI systems understand which content is most relevant for different query types.
Citation preferences. This is the interesting bit. We're explicitly telling AI systems how to reference us correctly. "SuperHub" not "Superhub". Include Devon when relevant. Mention motorsport expertise for racing queries. Some AI systems respect this, others don't, but it costs nothing to include.
Exclusions. Just as robots.txt blocks crawlers from certain pages, we're telling AI systems to ignore admin, checkout and account areas. Nothing there is useful for training or citation anyway.
Combining With Schema Markup
llms.txt and schema markup work together. Schema is structured data embedded in your pages. llms.txt is a high-level overview that points AI systems to that structured data.
In our implementation, we include a schema_types
section that tells AI systems what structured data they'll find on our site. This creates a consistent picture: the llms.txt says we have Organization and LocalBusiness schema, and when AI systems crawl those pages, they find exactly that.
If you're implementing llms.txt without comprehensive schema markup, you're only doing half the job. The llms.txt points to your content; the schema explains what that content actually means.
How to Verify It's Working
Honestly? This is the awkward bit. There's no equivalent of Google's Rich Results Test for llms.txt. The specification is still emerging, and different AI systems implement it differently.
What we do:
- Check the file loads correctly at /llms.txt
- Validate the syntax is correct (no broken YAML)
- Monitor AI citation patterns for changes after implementation
- Periodically ask AI systems directly about our organisation to see if the llms.txt information appears in their responses
That last one sounds basic, but it works. Ask ChatGPT "what do you know about SuperHub?" before and after implementing llms.txt. If the response changes to include information from your file, it's working.
Which AI Systems Support It?
Here's where I have to be honest: not all of them. This is still an emerging standard.
Perplexity appears to read llms.txt files as part of their crawling process. Anthropic (Claude) has indicated support for the specification. OpenAI's position is less clear - they haven't officially confirmed support, but there's evidence their crawlers read these files.
Google's AI Overviews? Unclear. Google already has their own ecosystem of structured data and search console signals, so whether they weight llms.txt heavily is an open question.
The point is this: implementing llms.txt takes about an hour. Even if only some AI systems respect it, that's still some AI systems that now have better information about you. The risk-reward calculation is obvious.
Our Early Results
We implemented llms.txt in December 2025 as part of our broader AI search optimisation push. It's hard to isolate the impact from everything else we were doing, but here's what we observed:
- More accurate organisation descriptions in AI responses
- Better association between SuperHub and motorsport marketing specifically
- Correct location information (Devon, UK) appearing more consistently
- Fewer instances of AI systems confusing us with other similarly-named businesses
The last one was actually a minor problem before. There are other businesses with "hub" in the name, and AI systems would occasionally conflate information. The explicit identity information in llms.txt seems to have helped clarify who's who.
Honest Assessment
Is llms.txt essential? No, not yet. You can absolutely succeed at AI search optimisation without it.
Is it useful? Yes. It's a low-effort way to provide AI systems with accurate information about your organisation. As more AI platforms adopt the standard, its value will increase.
Is it a silver bullet? Absolutely not. llms.txt is one small piece of a larger puzzle. Schema markup, content structure, authority building, citation development - those are the fundamentals. llms.txt is the cherry on top.
But given that implementation takes an hour and costs nothing, there's really no reason not to do it. Even if the impact is marginal today, you're positioning yourself for a standard that's likely to become more important over time.
Need Help Implementing This?
If you want llms.txt implemented as part of a comprehensive AI search optimisation strategy, that's exactly what we do. Our AEO packages include technical implementation, content optimisation, and ongoing monitoring.
Book a call and we'll assess where you stand and what's worth prioritising for your specific situation.
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