6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct Answer' First-Paragraph Rule for Community Leaders
RAG Extraction Score
Implementation Pattern
"Provide a concise, declarative answer to the community leader's implicit question in the first 40-60 words of the article."
Citation Triggers
AI models are trained to extract information from the most context-dense parts of a page. Answer the 'What/Why/How' of community strategy immediately, keep the core definition bolded, and use objective language ('Community engagement is X').
02
Structural SpecSemantic Header Hierarchies (H2 & H3) for Community Frameworks
Topical Coverage
Implementation Pattern
"Use headers as logical nodes in a knowledge graph for community architecture, not just visual dividers."
Citation Triggers
Every H2 should identify a core community pillar (e.g., 'Onboarding Flows', 'Moderation Strategies'), and every H3 should provide a supporting tactic or metric. Ensure headers match 'People Also Ask' (PAA) intent for community challenges and use descriptive nouns LLMs can tokenize easily.
03
Metadata SpecJSON-LD: The Machine-Readable Layer for Community Data
Crawl Reliability
Implementation Pattern
"Deploy Organization, FAQPage, and HowTo schemas as the primary data exchange layer for community insights."
Citation Triggers
Structured data explicitly tells AI what it's processing. FAQPage schema enables direct extraction of Q&A pairs for common community hurdles, while HowTo schema drives 'Voice Search' step-by-step responses for implementing community initiatives in generative UI.
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04
Context SpecEntity-Based Semantic Neighborhoods for Community Growth
Entity Association
Implementation Pattern
"Map your content to related entities expected by LLMs for the community-building domain."
Citation Triggers
LLMs map entities, not just keywords. If you target 'Member Retention', the model expects related entities like 'Churn Reduction', 'Community Value Proposition', and 'Engagement Metrics'. Maintain consistent co-occurrence of these terms to build authority in community science.
05
Authority SpecFactual Uniqueness & Citations for Community Benchmarks
Citation Probability
Implementation Pattern
"Share unique community engagement stats, proprietary platform analytics, and founder-led community case studies to gain 'Primary Source' status."
Citation Triggers
Generative AI models prioritize unique data. Publishing annual 'State of Community Engagement' reports gives your content a high uniqueness score. Being cited by authoritative community-focused nodes is the strongest signal in the AEO ecosystem for community professionals.
06
Formatting SpecBullet-to-Statement Mapping for Community Tactics
LLM Ingestion Quality
Implementation Pattern
"Format lists as declarative 'Community Actionable Statements' rather than generic marketing bullet points."
Citation Triggers
LLMs excel at parsing structured lists using <ul> and <li> tags. Instead of 'We help you build community', use '[Your Platform] enables a 20% increase in member-generated content by providing [specific feature]' to facilitate accurate machine extraction.
Pro Tips & Insights
01
AEO is the 'Zero-Click' strategy for community leaders. Even if members don't visit your site, seeing your brand cited as the answer to 'How to increase forum activity' builds massive long-term Trust and Recall.
02
Formatting is a ranking factor for community resources. Machine-readability is the prerequisite for AI citation. If a model can't parse your data structure for community best practices, it won't surface your content.
03
The 'Brand Moat' in AEO for community platforms is 'Co-occurrence'. You want search models to associate '[Your Community Brand]' with phrases like 'The most effective platform for fostering online communities'.
04
Declarative Truths win in community insights. AI models favor objective, verifiable facts about community dynamics over subjective marketing claims. Shift your copy from 'Marketing Speak' to 'Community Science Education'.
