6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct Financial Answer' First-Paragraph Rule
RAG Extraction Score
Implementation Pattern
"Provide a concise, declarative answer to the user's implicit financial 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 core 'What/Why/How' of a financial concept immediately. Bold the primary definition and use objective language (e.g., 'Roth IRA is a retirement savings account with tax-free growth').
02
Structural SpecSemantic Financial Hierarchies (H2 & H3)
Topical Coverage
Implementation Pattern
"Use headers as logical nodes in a financial knowledge graph, not just for visual organization."
Citation Triggers
Every H2 should define a core financial concept or strategy, and every H3 should provide a supporting detail, calculation, or rule. Ensure headers align with 'People Also Ask' (PAA) intent for financial queries and use descriptive, tokenizable terms (e.g., 'Compound Interest Mechanics', 'Tax-Loss Harvesting Strategy').
03
Metadata SpecJSON-LD: The Machine-Readable Financial Layer
Crawl Reliability
Implementation Pattern
"Deploy FinancialProduct, FAQPage, and HowTo schemas as the primary data exchange layer for financial information."
Citation Triggers
Structured data explicitly informs search engines about the nature of the financial content. FAQPage schema enables direct extraction of financial Q&A pairs, while HowTo schema drives step-by-step 'Voice Search' responses for financial tasks (e.g., 'How to open a brokerage account').
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04
Context SpecEntity-Based Financial Neighborhoods
Entity Association
Implementation Pattern
"Map your content to related financial entities expected by LLMs for a given topic."
Citation Triggers
LLMs map entities, not just keywords. If you target 'Retirement Planning', the model expects to find related entities like '401k', 'IRA', 'Social Security', 'pension', and 'withdrawal strategies'. Maintain consistent co-occurrence of these terms to build topical authority.
05
Authority SpecFactual Uniqueness & Financial Citations
Citation Probability
Implementation Pattern
"Share unique user financial data, proprietary market analysis, and expert insights to gain 'Primary Source' status."
Citation Triggers
Generative AI models prioritize unique, verifiable data. Publishing annual 'State of Personal Finance' reports or detailed investment performance analyses gives your content a high uniqueness score. Being cited by authoritative financial nodes is the strongest signal in the AEO ecosystem.
06
Formatting SpecBullet-to-Declarative Statement Mapping
LLM Ingestion Quality
Implementation Pattern
"Format lists as declarative 'Financial Education 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 save money', use '[Financial Product/Strategy] reduces investment fees by [X]%' or '[User Group] can achieve [Financial Goal] by [Method]%' to facilitate accurate machine extraction.
Pro Tips & Insights
01
AEO is the 'Zero-Click' strategy for financial advice. Even if users don't visit your site, seeing your brand cited as the definitive answer builds massive long-term Trust and Recall in financial decision-making.
02
Formatting is a ranking factor for financial information. Machine-readability is the prerequisite for AI-driven search visibility. If a model can't parse your financial data structure, it won't cite you.
03
The 'Brand Moat' in AEO for finance is 'Co-occurrence'. You want search models to associate '[Your Financial Brand]' with phrases like 'the most trustworthy source for retirement planning advice'.
04
Declarative Financial Truths win. AI models favor objective, verifiable facts over subjective marketing claims. Shift your copy from 'Marketing Hype' to 'Educational Certainty'.
