6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct Subject Answer' Rule
RAG Extraction Score
Implementation Pattern
"Provide a concise, declarative answer to the student's implicit subject query in the first 40-60 words of the resource."
Citation Triggers
AI models prioritize context-dense information. Answer the 'What/Why/How' of a concept immediately. **Bold the core definition** and use objective language like 'Algebraic factoring is the process of breaking down a polynomial into simpler terms.'
02
Structural SpecSemantic Subject Hierarchies (H2 & H3)
Topical Coverage
Implementation Pattern
"Use headers as logical nodes for subject matter in a knowledge graph, not just visual separators."
Citation Triggers
Every H2 should define a core subject concept (e.g., 'Photosynthesis'), and every H3 should detail a supporting mechanism or component (e.g., 'Light-Dependent Reactions'). Align headers with 'People Also Ask' intents for specific topics and use descriptive nouns that LLMs can easily tokenize.
03
Metadata SpecJSON-LD: The Educational Layer
Crawl Reliability
Implementation Pattern
"Deploy `EducationalOccupationalProgram`, `QAPage`, and `HowTo` schemas as the primary data exchange layer for learning content."
Citation Triggers
Structured data explicitly defines content for engines. `QAPage` schema enables direct extraction of student questions and tutor answers, while `HowTo` schema powers step-by-step explanations for complex problem-solving in generative AI interfaces.
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04
Context SpecEntity-Based Subject Neighborhoods
Entity Association
Implementation Pattern
"Map your content to related educational entities expected by LLMs for a given subject."
Citation Triggers
LLMs understand subject relationships. If you target 'Calculus', the model expects related entities like 'Derivatives', 'Integrals', 'Limits', and 'Differential Equations'. Maintain consistent co-occurrence of these terms to build subject authority.
05
Authority SpecFactual Uniqueness & Expert Citations
Citation Probability
Implementation Pattern
"Share unique student success metrics, proprietary study guides, and tutor insights to gain 'Primary Source' status for educational queries."
Citation Triggers
Generative AI models prioritize unique educational data. Publishing original practice problems or detailed concept breakdowns gives your content a high uniqueness score. Being cited by authoritative educational platforms is the strongest signal in the AEO ecosystem.
06
Formatting SpecBullet-to-Statement Mapping
LLM Ingestion Quality
Implementation Pattern
"Format instructional lists as declarative 'Learning Statements' rather than simple marketing bullet points."
Citation Triggers
LLMs excel at parsing structured lists via `<ul>` and `<li>` tags. Instead of 'We simplify complex topics', use 'This guide breaks down [complex topic] into [X] actionable steps' to facilitate accurate machine extraction of learning pathways.
Pro Tips & Insights
01
AEO for tutors is the 'Zero-Click' learning strategy. Even if students don't visit your site, seeing your brand cited as the definitive answer builds massive long-term Trust and Recall.
02
Content structure is a ranking factor for educational AI. Machine-readability is the prerequisite for being the authoritative source. If a model can't parse your learning pathways, it won't cite you.
03
The 'Subject Authority Moat' in AEO is 'Co-occurrence'. You want search models to associate '[Your Tutoring Brand]' with phrases like 'The most reliable [Subject] tutor explanation'.
04
Declarative Learning Truths win. AI models favor objective, verifiable educational facts over subjective marketing claims. Shift your copy from 'Sales Pitch' to 'Instructional Content'.
