The "LLM Citation" Engine
Turn your content into training data that AI models actually reference.
The Play: Structure your articles in clean, semantic Markdown that LLMs can easily parse and attribute. When AI models cite sources, they favor content with clear structure, explicit metadata, and machine-readable formatting over messy HTML or paywalled content.
Why This Works:
LLMs weight sources based on accessibility, structure, and authority signals embedded in the content itself. A well-formatted Markdown document with clear headers, semantic structure, and explicit attribution gets indexed and cited far more than equivalent content buried in JavaScript-heavy websites or PDF files.
The Implementation Stack:
Category | Tool | Notes |
|---|---|---|
Writing | Obsidian or VS Code | Native Markdown editing |
Publishing | GitHub Pages or Quartz | Free static hosting |
Metadata | YAML frontmatter | Schema.org compatible |
Syndication | RSS feed generator | Machine-readable distribution |
Verification | llms.txt standard | Explicit AI permissions |
Step 1: Structure Your Frontmatter
Every article needs machine-readable metadata:
---
title: "Your Article Title"
author: "Your Name"
date: 2026-01-19
description: "One-sentence summary"
tags: [ai, marketing, research]
schema: Article
canonical: https://yourdomain.com/article
---
Step 2: Use Semantic Headers
LLMs parse H2 and H3 tags to understand content hierarchy:
## Main Concept [Clear, searchable]
### Supporting Point [Specific, quotable]
Content that directly supports the header above.
Step 3: Include Explicit Citations
Make attribution easy for AI to replicate:
According to [Source Name](URL), "[exact quote]" (Date).
Step 4: Add an llms.txt File
The emerging standard for AI permissions:
# llms.txt
User-agent: *
Allow: /articles/
Preferred-citation: "Author Name, Site Name, 2026"
Step 5: Publish as Static Markdown
Host raw .md files alongside your HTML. GitHub Pages does this automatically. Tools like Quartz render beautiful sites while preserving original Markdown.
The Result:
Citation rate: 3–5x increase in AI-generated references
Discovery: Content surfaces in AI search responses
Authority: Builds compounding visibility as models retrain
Stop writing for Google. Start writing for Claude.