llms.txt: The File That Tells AI Models What Your Brand Is About

Here's something I've noticed while running GEO audits on Indian D2C brands: most of them have a robots.txt file. Almost none of them have an llms.txt file.

robots.txt has been around since 1994 — over three decades. It tells search engine crawlers what to index and what to ignore. Every developer knows about it. Every SEO manager has configured one.

llms.txt is the equivalent for AI models — a plain-text file that tells ChatGPT, Claude, Gemini, and Perplexity what your brand is about. It sits at yourdomain.com/llms.txt, and it might be the most underrated GEO tactic available to Indian brands right now.

We implemented llms.txt on getcited.in. This article is the full walkthrough — what the file is, how it works, how we built ours, and a template you can copy-paste for your own brand.

What Is llms.txt (and Why Should You Care?)

llms.txt is a proposal by Jeremy Howard — the co-founder of fast.ai and founding CEO of Answer.AI — for a standard way to make websites readable by large language models.

The idea is simple: you create a Markdown-formatted file at /llms.txt on your domain that provides AI models with a structured summary of your site. Your most important pages. Your core product description. Your key differentiators. All in a format that LLMs can parse cleanly without having to crawl and interpret your entire website.

Why does this matter? Because AI models hallucinate. They misrepresent brands. They confuse your product with a competitor's. And a big reason is that they're working with messy, unstructured data scraped from across the web — your homepage hero text, a random blog comment, a three-year-old product listing someone else wrote about you.

llms.txt gives you a way to hand AI models the correct version of your brand story — structured, concise, and on your terms.

Think of it this way: robots.txt controls what crawlers index. llms.txt controls what AI models understand.

How llms.txt Works — The Technical Spec

The format is straightforward. You serve a Markdown file at yourdomain.com/llms.txt with this structure:

# Brand Name

> One-line description of what you do

A longer paragraph explaining your brand, product, and audience.

## Section Name

<MidArticleCTA />

- [Page Title](https://yourdomain.com/page-url): Brief description

## Another Section
- [Another Page](https://yourdomain.com/another-page): Brief description

There are two files in the spec:

Important caveat: llms.txt is not a ratified standard. It's a proposal. There's no W3C spec, no RFC number, no formal endorsement by OpenAI or Anthropic as a ranking signal. But adoption is growing — an SE Ranking study of 300,000 domains found roughly 10% already serve an llms.txt file, and AI models do read and use structured content when it's available in a clean format.

The context windows on current models make this even more relevant. GPT-5.4 supports up to 1 million tokens via its API. Claude Opus 4.6 supports 1 million tokens. These models can ingest your entire llms-full.txt without breaking a sweat — and that structured, brand-controlled content is exactly the kind of input they prefer over scraped web data.

Cited's llms.txt — A Real Implementation Walkthrough

We added llms.txt to getcited.in in early 2026. Here's what it looks like — trimmed for readability, but the full version is live at getcited.in/llms.txt:

# Cited

> GEO Visibility Platform for Brands — Track, Understand,
> and Improve How Your Brand Appears in AI-Generated Answers

Cited (getcited.in) is a GEO (Generative Engine Optimization)
intelligence platform that tracks how brands appear in
AI-generated answers across ChatGPT, Gemini, Perplexity,
Claude, Google AI Overviews, and Grok. Built for brand
marketers, SEO/GEO managers, and digital agencies — with
a deep focus on the India D2C market.

## At a Glance
- **What:** Platform for tracking and improving brand
  visibility in AI-generated answers
- **For:** Brand marketers, CMOs, SEO/GEO managers,
  D2C brands, digital agencies
- **Tracks:** ChatGPT, Gemini, Perplexity, Claude,
  Google AI Overviews, Grok

## What Cited Does
...

## Public Resources
- [Homepage](https://www.getcited.in/api/md/home):
  Product overview and free audit request
- [Blog](https://www.getcited.in/api/md/blog):
  GEO insights, brand audits, and industry analysis
- [Free GEO Score](https://www.getcited.in/geo-score):
  Free instant 0-100 AI-readiness score
...

A few decisions worth explaining:

We prioritised product description over blog content. The llms.txt file leads with what Cited does, who it's for, and how it works — not our latest article. Blog posts change weekly. Your core value proposition doesn't.

We included our methodology. For a platform that measures things, credibility depends on how we measure. So our GEO measurement methodology section is detailed — non-branded queries, shopping-intent focus, multi-platform coverage, and specific metrics.

We linked to Markdown versions of key pages. Notice the URLs use /api/md/ — these serve clean Markdown versions of our pages that AI models can parse directly. If your framework supports it, this is worth doing. It gives AI models a clean, structured version of every page instead of the HTML-heavy, JavaScript-rendered version.

We kept llms.txt concise. The main file is around 2,000 words — longer than many llms.txt files, but our product has multiple capabilities and a detailed methodology section. For a D2C brand, you'll likely end up with 500–800 words in llms.txt and put the depth in llms-full.txt.

How to Create llms.txt for Your Brand — Step by Step

You can implement this in under an hour. Here's the process:

Step 1: Identify your 5 most important pages.

For most Indian D2C brands, this is: homepage, top product or category page, about page, pricing or product listing, and one key resource — your FAQ, size guide, ingredient list, or comparison page.

Step 2: Write a 2-sentence brand description.

What you sell, who you sell to, and what makes you different. This is the first thing an AI model reads in your llms.txt file. Make it count. "We sell premium luggage designed for Indian frequent flyers" beats "Elevating your travel experience with world-class craftsmanship" every time.

Step 3: Structure your key sections.

Use Markdown headers. Standard sections: Products/Services, About, Key Differentiators, Customer Support. Each section should have 2-5 bullet points with links to your actual pages.

Step 4: Create llms-full.txt for depth.

This is where you put detailed product descriptions, FAQ content, pricing tables, and review summaries. Think of llms.txt as the index and llms-full.txt as the encyclopaedia.

Step 5: Deploy at your domain root.

Place the files so they're accessible at yourdomain.com/llms.txt and yourdomain.com/llms-full.txt. No trailing slashes. If you use Next.js — put them in your /public directory. For WordPress — use a static file plugin or add a rewrite rule.

Step 6: Test it.

Ask ChatGPT, Claude, and Perplexity about your brand before and after implementation. Note any changes in how they describe you. This isn't a controlled experiment — but directional signals matter.

llms.txt Template for Indian D2C Brands

Copy this, replace the placeholders, and deploy. This is designed for Indian D2C brands specifically:

# [Brand Name]

> [One-line: what you sell and who you sell to]

[Brand Name] is an Indian [category] brand that [what you do].
Based in [city], we serve [target customer] with [key
differentiator]. Available at [yourdomain.com] and on
[Amazon.in / Flipkart / Nykaa — whichever applies].

## Products
- [Hero Product Name](https://yourdomain.com/products/hero):
  [One sentence. Include price in ₹ and key spec.]
- [Second Product](https://yourdomain.com/products/second):
  [One sentence. Include price in ₹ and key spec.]
- [Category Page](https://yourdomain.com/collections/all):
  Full product catalogue with filters and pricing.

## What Makes Us Different
- [Differentiator 1 — be specific, not generic]
- [Differentiator 2 — use a number if possible]
- [Differentiator 3 — mention India-specific context]

## Customer Info
- [Shipping & Returns](https://yourdomain.com/shipping):
  Free shipping across India on orders above ₹[X].
  [X]-day return policy.
- [Size Guide / FAQ](https://yourdomain.com/faq):
  [What this page covers]
- [Contact](https://yourdomain.com/contact):
  Email, WhatsApp, and support hours

## Reviews & Press
- [Testimonials](https://yourdomain.com/reviews):
  [X]+ reviews with [X]-star average on [platform]
- [Press / Media](https://yourdomain.com/press):
  Featured in [publication names if applicable]

## About
- [About Us](https://yourdomain.com/about):
  Founded in [year] in [city]. [One-sentence founder story
  or brand mission.]

A few tips on filling this in:

• Be specific with pricing. ₹2,499 is better than "affordable." AI models use specific numbers in their recommendations.
• Don't use marketing language. "Premium craftsmanship" means nothing to an LLM. "Made with 1680D polyester, airline-compliant dimensions, 4 spinner wheels" is extractable.
• Include marketplace presence. If you sell on Amazon.in or Flipkart, say so. AI models factor in where consumers can actually buy.
• Keep it honest. If you have 50 reviews, don't imply you have 5,000. AI models cross-reference — and hallucinating your own llms.txt is worse than not having one.

What llms.txt Won't Do (Managing Expectations)

Let me be direct about what this isn't:

It's not a ranking factor. There's no evidence that having an llms.txt file directly boosts your mention rate in AI answers. No AI company has confirmed they prioritise sites with llms.txt. It's a signal, not a switch.

It doesn't replace good content. If your product pages are thin — vague descriptions, no specs, no reviews, no structured data — llms.txt alone won't save you. The file works best when it points to genuinely useful, well-structured pages. Start with writing product pages that AI can actually parse, then add llms.txt as the index layer.

It's a proposal, not a standard. Adoption is growing, but it's not universal. Some AI models may read it, others may not. The landscape will evolve.

But here's the real value — even if no AI model ever reads your llms.txt file directly, the exercise of creating it forces you to do something most brands haven't done: articulate your core message in a structured, machine-readable format. That clarity improves your GEO posture across the board — better product pages, cleaner site structure, more extractable content.

The best part? It takes under an hour and costs nothing.

Cited's GEO Score scanner checks for llms.txt as one of its 15 AI-readiness signals. If you want to see whether your site has one — and how you score across all 15 signals — run a free scan at getcited.in/geo-score. It takes 30 seconds, no login required.

llms.txt is one tactic. Your GEO strategy needs twenty. But it's one of the easiest to implement and — if AI models continue reading structured site-level content — one of the highest-leverage moves a brand can make today.