The LLMO Framework: A Standard for AI Discoverability

The LLMO Framework defines six core components — Knowledge Clarity, Structural Formatting, Retrieval Signals, Authority Signals, Citation Signals, and Coherence Signals — that together determine whether AI systems can discover, understand, and accurately cite your content. Each component is scored 0-3, for a maximum site score of 18 points.

New to the concept? Start with What is LLMO?, or jump straight to the 30-minute Quickstart.

What are the six components of the LLMO Framework?

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, scoped per page
• Provide llms.txt for AI-specific content
• Verify JSON-LD actually emits in the served HTML

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

6. Coherence Signals

Does the same fact tell the same story across every surface AI reads?

• Single source of truth for every numeric or factual claim
• AI-only surfaces (llms.txt, /ai/*.md) generated from the same data as HTML
• Canonical host and trailing-slash policy enforced everywhere
• No duplicate JSON-LD entities for the same @id

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: 18 points (6 components × 3 points each)

Self-Assessment Checklist

Score your own site against each component. Treat anything you can confidently check off as 1 point; aim for 3 boxes per component to reach the top score.

1. Knowledge Clarity (max 3)

• Every page leads with a one-sentence answer to its primary question (Answer-first)
• Domain-specific terms are defined on first use (no unexplained jargon)
• Each paragraph holds a single idea (no multi-claim paragraphs)

2. Structural Formatting (max 3)

• Pages use semantic H1 → H2 → H3 hierarchy with no heading skips
• Every meaningful page emits page-relevant JSON-LD; site-wide layout emits only Organization / WebSite / Person
• Build pipeline verifies JSON-LD actually parses in dist/ HTML

3. Retrieval Signals (max 3)

• /llms.txt exists at the site root and lists key pages
• /ai/ directory ships clean Markdown for each major topic (and per-language if the site is multilingual)
• robots.txt explicitly allows GPTBot, ClaudeBot, PerplexityBot, Google-Extended; sitemap.xml is reachable

4. Authority Signals (max 3)

• Author has a verifiable bio with sameAs links to LinkedIn / GitHub / X / publication profiles
• The same identity (name, role, topic focus) appears consistently across at least 3 platforms
• Site links to original research, books, or papers the author has actually published

5. Citation Signals (max 3)

• Every claim that uses a number cites a source by name and year
• Each content page (article, guide, case study) exposes both datePublished and dateModified (in JSON-LD or visible meta). Site root and error pages are exempt
• Comparison content references industry standards (W3C, RFC, ISO, schema.org) by name and link

6. Coherence Signals (max 3)

• Each numeric / factual claim has a single canonical source file referenced everywhere else
• AI surfaces (llms.txt, /ai/*.md, URL.md endpoints) are generated from the same data as the HTML
• CI checks for cross-file drift on key metrics; no duplicate JSON-LD entity for the same @id

Scoring guide

Total	Band
16–18	Production-grade — actively cited by AI systems
11–15	Good — visible to AI but inconsistent
6–10	Partial — significant gaps in retrieval, authority, or coherence
0–5	Invisible — start with /llms.txt, robots.txt, and JSON-LD

Want a higher score? Each component page (Knowledge Clarity, Structural Formatting, Retrieval Signals, Authority Signals, Citation Signals, Coherence Signals) lists the specific implementations that move the score from 1 → 2 → 3.