The LLMO Framework: A Standard for AI Discoverability

The LLMO Framework defines five core components that determine whether AI systems can discover, understand, and accurately cite your content.

The Five Components

1. Knowledge Clarity

Is your content clear enough for AI to understand and summarize accurately?

• Use plain, unambiguous language
• Define key terms explicitly
• Provide structured facts (who, what, when, where)
• Avoid jargon without explanation

2. Structural Formatting

Is your content structured for machine consumption?

• Use semantic HTML and Markdown
• Implement JSON-LD structured data
• Provide llms.txt for AI-specific content
• Organize content hierarchically

3. Retrieval Signals

Can AI systems find your content when they need it?

• Ensure crawlability (robots.txt, sitemap.xml)
• Provide machine-readable endpoints (/ai/, .md files)
• Implement the llms.txt standard
• Make content available via APIs where possible

4. Authority Signals

Does your content demonstrate expertise and trustworthiness?

• Author attribution with verifiable credentials
• Cross-platform presence (GitHub, LinkedIn, publications)
• Consistent information across all platforms
• Evidence-based claims with citations

5. Citation Signals

Does your content provide references that AI can verify?

• Link to primary sources
• Include publication dates
• Provide version information
• Reference academic papers and official documentation

Scoring

Each component can be scored on a scale of 0-3:

Score	Level	Description
0	None	Component not addressed
1	Basic	Minimal implementation
2	Good	Solid implementation with room for improvement
3	Excellent	Best-practice implementation

Maximum score: 15 points (5 components × 3 points each)