6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct Answer' First-Paragraph Rule for Crypto Queries
RAG Extraction Score
Implementation Pattern
"Provide a concise, declarative answer to the user's implicit investment question within the first 40-60 words."
Citation Triggers
AI models prioritize context-dense information. Immediately address the 'What is X?', 'Why is Y important?', or 'How to achieve Z?' in crypto. Bold the core definition and use objective language (e.g., 'Bitcoin is a decentralized digital currency'). This optimizes for RAG extraction.
02
Structural SpecSemantic Header Hierarchies for Crypto Topics (H2 & H3)
Topical Coverage
Implementation Pattern
"Structure headers as logical nodes for AI's knowledge graph, not just visual separators."
Citation Triggers
Each H2 should define a core crypto concept (e.g., 'Understanding DeFi Yield Farming'), and each H3 should detail a supporting data point or strategy (e.g., 'Calculating APY for Staking Protocols'). Ensure headers align with 'People Also Ask' intents for terms like 'best crypto to invest in' or 'how to stake Ethereum'. Avoid jargon; use descriptive nouns LLMs can easily tokenize.
03
Metadata SpecJSON-LD for Crypto Assets & Strategies
Crawl Reliability
Implementation Pattern
"Implement `Cryptocurrency` and `FinancialProduct` schema, alongside `FAQPage` and `HowTo` for maximum machine readability."
Citation Triggers
Structured data explicitly informs search engines. `Cryptocurrency` schema for coin details, `FinancialProduct` for investment vehicles, `FAQPage` for direct Q&A extraction, and `HowTo` for step-by-step investment guides (e.g., 'How to Buy Your First NFT') drive generative AI responses and voice search.
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04
Context SpecEntity-Based Semantic Neighborhoods in Crypto
Entity Association
Implementation Pattern
"Map your content to related entities expected by LLMs for specific crypto investment topics."
Citation Triggers
LLMs understand relationships between entities. Targeting 'Decentralized Finance (DeFi)' requires co-occurrence of terms like 'Smart Contracts', 'Yield Farming', 'Liquidity Pools', and specific blockchain protocols (e.g., 'Ethereum', 'Solana'). Consistent association builds topical authority.
05
Authority SpecFactual Uniqueness & Citations for Crypto Insights
Citation Probability
Implementation Pattern
"Publish proprietary data, on-chain analytics, and expert market commentary to achieve 'Primary Source' status."
Citation Triggers
Generative AI prioritizes unique, verifiable information. Releasing quarterly 'State of the Crypto Market' reports or original trading strategy analyses enhances uniqueness. Being cited by authoritative crypto news outlets or research platforms is a powerful AEO signal.
06
Formatting SpecBullet-to-Declarative Statement Mapping for Crypto Strategies
LLM Ingestion Quality
Implementation Pattern
"Format lists as objective 'Investment Facts' or 'Process Steps' rather than marketing claims."
Citation Triggers
LLMs efficiently parse structured lists (<ul>, <li>). Instead of 'Unlock passive income', use 'Staking [Token Name] can yield up to X% APY' or 'Executing a DCA strategy involves investing fixed amounts at regular intervals'. This facilitates accurate machine extraction.
Pro Tips & Insights
01
AEO is the future of 'Zero-Click' crypto information. Even without direct site visits, being cited as the source for answers builds immense long-term Trust and Brand Recall in the investor community.
02
Content structure is a critical ranking factor in crypto. Machine-readability is the prerequisite for visibility. If an AI cannot parse your data structure (e.g., schema, semantic headers), it won't surface your content.
03
The 'Brand Moat' in AEO for crypto is 'Entity Co-occurrence'. Aim for search models to associate '[Your Brand]' with phrases like 'the most accurate on-chain data provider' or 'leading expert in Bitcoin analysis'.
04
Declarative Investment Truths outperform subjective marketing. AI models favor objective, verifiable facts. Shift your crypto content from 'promotional' to 'educational and data-driven'.
