What is LLMs.txt? Complete Guide to Creating LLMs.txt Files for Your Website

The emergence of LLMs.txt (commonly stylized as llms.txt) files represents a pivotal adaptation in web standards to accommodate the rise of large language models (LLMs), bridging the gap between traditional web architecture and AI-driven information retrieval. As AI tools like ChatGPT, Claude, and Perplexity increasingly integrate with search and content synthesis, llms.txt offers a proactive mechanism for site owners to curate and optimize their data for machine consumption, potentially influencing how content appears in AI-generated responses.

This comprehensive guide covers everything you need to know about llms.txt files: their origins, technical mechanics, content structure, creation methodologies, and practical implementation strategies. Whether you're a developer looking to optimize your documentation site or a content creator wanting to improve AI visibility, this guide provides actionable insights based on industry proposals, real-world implementations, and community feedback.

Table of Contents

• Understanding LLMs.txt Files
• Historical Context and Origins
• How LLMs.txt Files Work
• Content Structure and Format
• How to Create an LLMs.txt File
• Building a Next.js LLMs.txt Generator
• Best Practices and Considerations
• Future of LLMs.txt Files

Understanding LLMs.txt Files

What is llms.txt? An llms.txt file is a markdown-formatted guide placed at your website's root (e.g., https://example.com/llms.txt) that helps AI models and LLMs efficiently understand and access your site's content. Think of it as a curated "menu" for AI agents—similar to how robots.txt controls crawler access, but focused on guiding AI to the most relevant content rather than restricting access.

Key Benefits:

• Improved AI Context: Provides structured summaries and links to key content, helping LLMs process your site without full HTML scraping
• Token Efficiency: Points to markdown versions of pages, which are cleaner and more token-efficient than raw HTML
• Content Curation: Allows you to highlight your most important content for AI consumption
• Future-Proofing: Positions your site for emerging AI-powered search and discovery mechanisms

Current Adoption Status: While llms.txt has seen growing adoption since its proposal in September 2024, evidence suggests it may not yet significantly impact AI visibility. Some tools and platforms (like GitBook, Drupal, and Perplexity) have implemented support, but adoption remains experimental and uneven across the web.

Historical Context and Origins

The concept of llms.txt emerged from real challenges in AI-web interactions. In September 2024, Jeremy Howard, co-founder of Answer.AI and a key figure in AI education through fast.ai, publicly proposed llms.txt as a solution to inherent limitations in LLMs.

The Problem LLMs.txt Solves

LLMs face several constraints when processing websites:

• Limited Context Windows: Models typically have token limits (thousands of tokens), making it inefficient to process entire websites
• HTML Clutter: Raw HTML contains navigation, ads, JavaScript, and other elements that consume tokens without adding value
• Lack of Structure: AI models struggle to identify the most important content on a page

Howard's proposal built on established web conventions like:

• robots.txt (for crawler permissions, established since 1994)
• sitemaps.xml (for search engine indexing)

But shifted focus to curation for AI inference rather than training or broad discovery.

Evolution and Community Response

The proposal quickly evolved into a community-driven initiative with implementations in projects like:

• FastHTML
• nbdev
• GitBook
• Drupal

However, opinions vary on its immediate value:

Supporters argue it's an innovative solution that:

• Improves context quality for AI responses
• Reduces token waste
• Provides better control over how AI perceives your content

Critics suggest:

• No proven impact on AI visibility or rankings yet
• Potentially premature without major AI provider mandates
• Risk of becoming a "spam magnet" if misused for SEO manipulation

Recent analyses, such as SE Ranking's review of 300,000 sites, found no correlation with improved AI performance, suggesting adoption remains experimental. However, companies like Perplexity and Anthropic have shown support, indicating potential for wider integration.

How LLMs.txt Files Work

At its core, an llms.txt file functions as an index or "menu" for AI agents, hosted at the root of a domain (e.g., https://example.com/llms.txt) or a subpath like /docs/llms.txt.

Operational Flow

1. Discovery: When an LLM or AI agent queries a site, it can first retrieve the llms.txt file
2. Parsing: The file provides a structured overview, avoiding the need to crawl and parse raw HTML
3. Content Access: Points to markdown (.md) versions of key pages, which are cleaner and more token-efficient
4. Context Assembly: Tools parse the file and expand it into context-ready formats optimized for specific models

Technical Implementation

File Location: Place the file at your site's root:

• https://example.com/llms.txt (recommended)
• https://example.com/docs/llms.txt (for documentation sites)

Markdown Endpoints: The file should link to .md versions of pages. Common practices include:

• Appending .md to URLs (e.g., page.html.md)
• Serving markdown directly from your CMS or static site generator
• Using plugins in frameworks like VitePress or Docusaurus

Processing Tools: Community tools help expand llms.txt into usable formats:

• llms_txt2ctx CLI: Available in Python and JavaScript, parses files and creates context-ready outputs
• llms-ctx.txt: Concise version for quick context
• llms-ctx-full.txt: Detailed version with embedded content
• XML-like formats: Optimized for models like Claude

Complementary Standards

llms.txt works alongside other web standards:

• robots.txt: While robots.txt might block AI crawlers, llms.txt invites targeted access
• sitemaps.xml: Provides structure for traditional search engines
• llms-full.txt: Some sites maintain extended versions with exhaustive content dumps

Content Structure and Format

The content of an llms.txt file is deliberately concise and hierarchical, prioritizing expert-level summaries to fit within LLM constraints.

Required Elements

1. H1 Header (Required)

# Project Name

Identifies the site or project.

2. Blockquote Summary (Optional but Recommended)

> Brief description providing an overview of the project or site.

Provides quick context for AI models.

3. Details Section (Optional) Free-form paragraphs with usage notes, warnings, or background information.

4. Categorized Links (Optional but Recommended)

## Section Name

- [Title](https://url.md): Brief description or notes.

5. Optional Marker (Recommended for Secondary Content)

## Optional

Allows AI models to skip less critical sections for efficiency.

Complete Example Structure

Here's an example from FastHTML that demonstrates the full structure:

# FastHTML

> FastHTML is a python library which brings together Starlette, Uvicorn, HTMX, and fastcore's "FastTags" into a library for creating server-rendered hypermedia applications.

Important notes:

- Although parts of its API are inspired by FastAPI, it is _not_ compatible with FastAPI syntax and is not targeted at creating API services.
- FastHTML is compatible with JS-native web components and any vanilla JS library, but not with React, Vue, or Svelte.

## Docs

- [FastHTML quick start](https://fastht.ml/docs/tutorials/quickstart_for_web_devs.html.md): A brief overview of many FastHTML features.
- [HTMX reference](https://github.com/bigskysoftware/htmx/blob/master/www/content/reference.md): Brief description of all HTMX attributes, CSS classes, headers, events, extensions, js lib methods, and config options.

## Examples

- [Todo list application](https://github.com/AnswerDotAI/fasthtml/blob/main/examples/adv_app.py): Detailed walk-thru of a complete CRUD app in FastHTML showing idiomatic use of FastHTML and HTMX patterns.

## Optional

- [Starlette full documentation](https://gist.githubusercontent.com/jph00/809e4a4808d4510be0e3dc9565e9cbd3/raw/9b717589ca44cedc8aaf00b2b8cacef922964c0f/starlette-sml.md): A subset of the Starlette documentation useful for FastHTML development.

Content Guidelines

Best Practices:

• Keep content concise and human-readable
• Avoid jargon when possible
• Focus on high-value resources like documentation or APIs
• Ensure all links point to accessible markdown files
• Update regularly as your site evolves

How to Create an LLMs.txt File

Creating an llms.txt file is straightforward and accessible, whether you're working with a static site, CMS, or custom application.

Method 1: Manual Creation

Step 1: Draft the File

1. Open a text editor
2. Create a new file named llms.txt
3. Follow the structure outlined in the previous section
4. Ensure all links point to .md files

Step 2: Host the File Upload to your site's root directory via:

• Web host's file manager
• SFTP/FTP client
• Git repository (for static sites)
• CMS file upload feature

Step 3: Implement Markdown Endpoints Configure your server or CMS to serve .md versions:

• Static Site Generators: Most (Next.js, Gatsby, Hugo) can serve markdown
• CMS Plugins: Use plugins in WordPress, Drupal, or other CMS platforms
• Framework Plugins: VitePress, Docusaurus have built-in markdown support

Step 4: Test Accessibility Verify the file is accessible:

curl https://your-site.com/llms.txt

Method 2: Automated Generation Tools

Several tools can automate llms.txt creation:

1. Firecrawl

• Crawls a URL automatically
• Uses GPT-4o-mini to generate the file
• Access via API: https://llmstxt.firecrawl.dev/{url}?FIRECRAWL_API_KEY=your_key

2. SEOmator

• Free online generator
• Provides templates and suggestions

3. CMS Plugins

• AIOSEO (WordPress): Includes llms.txt generation feature
• GitBook: Automatic generation for documentation sites
• Mintlify: Documentation platform with built-in support

4. Custom Scripts You can create your own generator using:

• Site crawlers (Cheerio, Puppeteer)
• Content analysis APIs
• Template engines

Method 3: Integration into Build Process

For developers, integrate llms.txt generation into your deployment pipeline:

Next.js Example:

// scripts/generate-llms-txt.js
const fs = require('fs')
const path = require('path')

const llmsTxtContent = `# Your Site Name

> Your site description.

## Main Content
- [Home](https://yoursite.com/index.md): Welcome page.
`

fs.writeFileSync(path.join(process.cwd(), 'public', 'llms.txt'), llmsTxtContent)

Add to package.json:

{
  "scripts": {
    "build": "next build && node scripts/generate-llms-txt.js"
  }
}

Validation and Testing

After creating your llms.txt file:

1. Validate Structure: Use validators at llmstxt.org
2. Test with LLMs: Try asking ChatGPT or Claude to read your llms.txt
3. Check Links: Ensure all markdown links are accessible
4. Monitor Updates: Update the file when your site structure changes

Building a Next.js LLMs.txt Generator

Creating a Next.js web application for generating llms.txt files empowers users to quickly create these files for any website. Here's a step-by-step implementation guide.

Project Setup

1. Initialize Next.js Project:

npx create-next-app@latest llm-txt-generator --typescript
cd llm-txt-generator

2. Install Dependencies:

pnpm install axios react-markdown

Basic Generator Implementation

Create the Generator Page (app/page.tsx or pages/index.tsx):

'use client'

import { useState } from 'react'
import axios from 'axios'

export default function Home() {
  const [url, setUrl] = useState('')
  const [generatedTxt, setGeneratedTxt] = useState('')
  const [loading, setLoading] = useState(false)
  const [error, setError] = useState('')

  const handleGenerate = async () => {
    if (!url) {
      setError('Please enter a URL')
      return
    }

    setLoading(true)
    setError('')

    try {
      // Option 1: Use Firecrawl API (requires API key)
      const response = await axios.get(`https://llmstxt.firecrawl.dev/${encodeURIComponent(url)}`, {
        params: {
          FIRECRAWL_API_KEY: process.env.NEXT_PUBLIC_FIRECRAWL_API_KEY,
        },
      })

      setGeneratedTxt(response.data)
    } catch (err) {
      // Fallback: Generate basic template
      setGeneratedTxt(generateBasicTemplate(url))
      setError('Using template format. For full generation, API key required.')
    }

    setLoading(false)
  }

  const generateBasicTemplate = (siteUrl: string) => {
    return `# ${new URL(siteUrl).hostname}

> Website description - update this with your site's purpose.

## Main Content
- [Home](${siteUrl}/index.md): Homepage content.
- [About](${siteUrl}/about.md): About page.

## Optional
- [Blog](${siteUrl}/blog.md): Blog posts and articles.
`
  }

  const downloadFile = () => {
    const blob = new Blob([generatedTxt], { type: 'text/plain' })
    const url = URL.createObjectURL(blob)
    const a = document.createElement('a')
    a.href = url
    a.download = 'llms.txt'
    document.body.appendChild(a)
    a.click()
    document.body.removeChild(a)
    URL.revokeObjectURL(url)
  }

  return (
    <div className="container mx-auto max-w-4xl px-4 py-8">
      <h1 className="mb-6 text-3xl font-bold">LLMs.txt Generator</h1>

      <div className="mb-6">
        <label htmlFor="url" className="mb-2 block font-medium">
          Website URL
        </label>
        <input
          id="url"
          type="url"
          value={url}
          onChange={(e) => setUrl(e.target.value)}
          placeholder="https://example.com"
          className="w-full rounded-lg border px-4 py-2"
        />
      </div>

      <button
        onClick={handleGenerate}
        disabled={loading}
        className="rounded-lg bg-blue-600 px-6 py-2 text-white hover:bg-blue-700 disabled:opacity-50"
      >
        {loading ? 'Generating...' : 'Generate LLMs.txt'}
      </button>

      {error && (
        <div className="mt-4 rounded border border-yellow-400 bg-yellow-100 p-3">{error}</div>
      )}

      {generatedTxt && (
        <div className="mt-8">
          <div className="mb-4 flex items-center justify-between">
            <h2 className="text-xl font-semibold">Generated File</h2>
            <button
              onClick={downloadFile}
              className="rounded bg-green-600 px-4 py-2 text-white hover:bg-green-700"
            >
              Download
            </button>
          </div>
          <pre className="overflow-x-auto rounded-lg bg-gray-100 p-4">
            <code>{generatedTxt}</code>
          </pre>
        </div>
      )}
    </div>
  )
}

Enhanced Features

Add URL Validation:

const isValidUrl = (string: string) => {
  try {
    new URL(string)
    return true
  } catch (_) {
    return false
  }
}

Add Custom Sections:

const [sections, setSections] = useState({
  docs: true,
  blog: true,
  api: false,
})

Add Preview with React Markdown:

import ReactMarkdown from 'react-markdown'

// In your component
;<ReactMarkdown>{generatedTxt}</ReactMarkdown>

Deployment Considerations

• Environment Variables: Store API keys securely in .env.local
• Rate Limiting: Implement rate limiting for API calls
• Error Handling: Provide fallback templates when APIs fail
• Hosting: Deploy to Vercel for easy Next.js hosting

Best Practices and Considerations

Content Strategy

Do:

• ✅ Keep summaries concise and informative
• ✅ Focus on high-value content (docs, APIs, guides)
• ✅ Update regularly as your site evolves
• ✅ Test with actual LLMs to verify effectiveness
• ✅ Ensure all linked markdown files are accessible

Don't:

• ❌ Stuff keywords for SEO manipulation
• ❌ Include low-value or duplicate content
• ❌ Link to broken or inaccessible pages
• ❌ Create overly long files that exceed context limits
• ❌ Forget to update when site structure changes

Technical Considerations

Respect robots.txt: When crawling sites for generation, always respect robots.txt to avoid legal issues.

Markdown Availability: Ensure your site can serve markdown versions of pages. Common approaches:

• Static site generators (Next.js, Gatsby, Hugo)
• CMS markdown plugins
• Custom API endpoints serving markdown

File Size: Keep llms.txt files reasonably sized. If content grows, use the "Optional" section marker to allow truncation.

Security: If building a generator tool:

• Validate and sanitize user input
• Implement rate limiting
• Use secure API key storage
• Respect CORS policies

SEO and AI Visibility

Current Reality: As of late 2024, there's no proven correlation between llms.txt files and improved AI visibility or search rankings. However:

• Early adoption may provide advantages if standards mature
• Improves content structure and organization
• Demonstrates forward-thinking approach to AI integration

Balanced Approach:

• Implement llms.txt as part of a broader content strategy
• Don't rely solely on llms.txt for AI visibility
• Focus on creating high-quality, well-structured content
• Monitor industry developments and adjust strategy accordingly

Future of LLMs.txt Files

The future of llms.txt files depends on several factors:

Potential Developments

1. Standardization: If major AI providers (OpenAI, Anthropic, Google) formally adopt llms.txt, it could become a web standard similar to robots.txt.

2. Enhanced Tools: Expect more sophisticated generation tools, validators, and CMS integrations as adoption grows.

3. Evolution of Format: The format may evolve based on community feedback and AI model requirements.

4. Integration with AI Search: As AI-powered search becomes mainstream, llms.txt could play a crucial role in content discovery.

Monitoring Industry Trends

Stay informed about:

• Updates from Answer.AI and Jeremy Howard
• Adoption by major platforms (GitBook, Drupal, etc.)
• Research on effectiveness and impact
• Community discussions and proposals

Resources and References

• Official Proposal: llms-txt: The /llms.txt file
• Original Post: Jeremy Howard's proposal
• Validators: Use llmstxt.org for validation
• Examples: Check implementations on FastHTML, GitBook, and other early adopters

LLMs.txt files represent an innovative approach to bridging traditional web architecture with AI-driven content discovery. While adoption remains experimental and effectiveness is still being evaluated, creating an llms.txt file is a low-effort, high-potential investment in your site's future AI compatibility. Whether you're optimizing documentation, improving content structure, or preparing for emerging AI search mechanisms, implementing llms.txt demonstrates a forward-thinking approach to web development in the age of AI.

Remember: Focus on creating valuable, well-structured content first. LLMs.txt files are a tool to help AI understand your content better—they're not a replacement for quality content creation. As the landscape evolves, staying informed and adaptable will be key to leveraging these emerging standards effectively.