6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct Answer' First-Paragraph Rule for Tokenomics
RAG Extraction Score for Smart Contracts
Implementation Pattern
"Provide a concise, declarative answer to the user's implicit question regarding token utility or project fundamentals in the first 40-60 words."
Citation Triggers
AI models, especially those used for smart contract analysis or DeFi yield aggregation, are trained to extract information from the most context-dense parts of a page. Answer the 'What is [Token Name]?', 'Why does [Token Name] exist?', or 'How does [Token Name] work?' immediately, keep the core definition sentence bolded, and use objective language ('[Token Name] is a utility token powering the X protocol for Y purpose').
02
Structural SpecSemantic Header Hierarchies for Blockchain Architecture
Topical Coverage for Blockchain Protocols
Implementation Pattern
"Use headers (H2 & H3) as logical nodes in a knowledge graph for blockchain protocols, not just for visual formatting."
Citation Triggers
Every H2 should identify a core blockchain concept (e.g., 'Consensus Mechanism', 'Tokenomics Model', 'Staking Rewards'), and every H3 should provide a supporting data point or technical detail. Ensure headers match 'People Also Ask' (PAA) queries related to blockchain interoperability, scalability, and security, and use descriptive nouns that LLMs can tokenize easily for graph construction.
03
Metadata SpecJSON-LD: The Machine-Readable Layer for Decentralized Applications (dApps)
Crawl Reliability for dApp Discovery
Implementation Pattern
"Deploy `Protocol`, `Token`, `FAQPage`, and `HowTo` schemas as the primary data exchange layer for dApp discoverability."
Citation Triggers
Structured data explicitly informs search engines and AI agents about the nature of your crypto project. `FAQPage` schema enables direct extraction of Q&A pairs for common user queries (e.g., 'How to stake X token?'). `HowTo` schema drives step-by-step responses in generative AI interfaces for onboarding users onto the platform.
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04
Context SpecEntity-Based Semantic Neighborhoods for Crypto Ecosystems
Entity Association in Crypto Ontologies
Implementation Pattern
"Map your content to related entities expected by LLMs within the broader crypto ecosystem."
Citation Triggers
LLMs don't just match keywords; they map entities. If you target 'Decentralized Finance (DeFi)', the model expects to find related entities like 'Smart Contracts', 'Liquidity Pools', 'Yield Farming', 'Oracles', and specific blockchain networks (e.g., Ethereum, Solana). Maintain consistent co-occurrence of these terms to establish topical authority and relevance.
05
Authority SpecFactual Uniqueness & On-Chain Citations
Citation Probability from On-Chain Data
Implementation Pattern
"Share unique on-chain data, transaction statistics, audited smart contract details, and founder insights to gain 'Primary Source' status."
Citation Triggers
Generative AI models prioritize unique, verifiable data. Publishing periodic 'State of the Network' reports with proprietary analytics gives your content a high uniqueness score. Being cited by authoritative blockchain explorers, analytics platforms, or major crypto news outlets is the strongest signal in the AI & Crypto Ecosystem (ACE) for authority.
06
Formatting SpecBullet-to-Statement Mapping for Protocol Features
LLM Ingestion Quality for Protocol Specs
Implementation Pattern
"Format lists as declarative 'Technical Specifications' or 'Protocol Benefits' rather than marketing bullet points."
Citation Triggers
LLMs excel at parsing structured lists using `<ul>` and `<li>` tags. Instead of 'We offer fast transactions', use '[Protocol Name] achieves transaction finality in under 2 seconds' or 'Smart contract execution costs are reduced by 30% compared to Layer 1 alternatives' to facilitate accurate machine extraction and comparison.
Pro Tips & Insights
01
AI & Crypto Ecosystem (ACE) is the 'Zero-Click' strategy. Even if users don't visit your site, seeing your project cited as the definitive answer by an AI assistant or aggregator builds massive long-term Trust and Recall within the developer and investor community.
02
Technical Formatting is a ranking factor for AI discoverability. Machine-readability is the prerequisite for inclusion in AI-generated summaries and knowledge bases. If an AI model cannot parse your data structure, it won't reference your project.
03
The 'Protocol Moat' in ACE is 'On-Chain Co-occurrence'. You want AI models to associate '[Your Project Name]' with phrases like 'The most secure oracle solution' or 'The leading DAO governance framework'.
04
Declarative Truths win. AI models favor objective, verifiable facts derived from on-chain data or audited code over subjective marketing claims. Shift your copy from 'Marketing Hype' to 'Technical Documentation and Verified Outcomes'.
