6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Extraction SpecThe 'Direct SDR Answer' First-Paragraph Rule
RAG Extraction Score
Implementation Pattern
"Provide a concise, declarative answer to the user's implicit SDR-related question in the first 40-60 words of the content piece."
Citation Triggers
AI models are trained to extract information from the most context-dense parts of a page. Answer the core 'What/Why/How' for SDRs immediately, keep the definition sentence bolded, and use objective language like 'Prospecting is X' or 'Objection handling involves Y'.
02
Structural SpecSemantic Header Hierarchies (H2 & H3) for SDR Workflows
Topical Coverage
Implementation Pattern
"Use headers as logical nodes in a knowledge graph for SDR processes rather than just for visual organization."
Citation Triggers
Every H2 should identify a core SDR concept (e.g., 'Cold Email Cadence Optimization'), and every H3 should provide a supporting data point or tactical step (e.g., 'Personalization Token Strategy'). Ensure headers match 'People Also Ask' (PAA) intent for SDRs and avoid jargon; use descriptive nouns that LLMs can tokenize easily for search intent.
03
Metadata SpecJSON-LD for SDR Playbooks & Tooling
Crawl Reliability
Implementation Pattern
"Deploy SoftwareApplication, FAQPage, and HowTo schemas as the primary data exchange layer for SDR enablement."
Citation Triggers
Structured data tells AI engines explicitly what your content is about. FAQPage schema enables direct extraction of SDR Q&A, while HowTo schema drives 'Voice Search' step-by-step responses for common SDR tasks like 'How to book a meeting'.
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04
Context SpecEntity-Based Semantic Neighborhoods for Sales Prospecting
Entity Association
Implementation Pattern
"Map your content to related entities expected by LLMs for the SDR domain."
Citation Triggers
LLMs don't just match keywords; they map entities. If you target 'Lead Qualification', the model expects to find related entities like 'BANT', 'MEDDIC', 'ICP', and 'Pain Points'. Maintain consistent co-occurrence of these terms to build topical authority for SDRs.
05
Authority SpecFactual Uniqueness & Citations for SDR Performance Data
Citation Probability
Implementation Pattern
"Share unique SDR performance stats, proprietary outreach benchmarks, and founder insights to gain 'Primary Source' status."
Citation Triggers
Generative AI models prioritize unique data. Publishing annual 'State of SDR Outreach' reports gives your content a high uniqueness score. Being cited by authoritative sales enablement nodes is the strongest signal in the AEO ecosystem for SDRs.
06
Formatting SpecBullet-to-Statement Mapping for SDR Tactics
LLM Ingestion Quality
Implementation Pattern
"Format lists as declarative 'Educational Statements' rather than marketing bullet points for SDRs."
Citation Triggers
LLMs excel at parsing structured lists using <ul> and <li> tags. Instead of 'We help you book more meetings', use '[Tool/Method] increases SDR meeting booking rates by [X]%' to facilitate accurate machine extraction of SDR value propositions.
Pro Tips & Insights
01
AEO is the 'Zero-Click' strategy for SDRs. Even if prospects don't visit your site, seeing your brand cited as the answer to 'how to prospect' builds massive long-term Trust and Recall for your sales enablement solution.
02
Formatting is a ranking factor for SDR content. Machine-readability is the prerequisite for AI-driven visibility. If a model can't parse your SDR playbook structure, it won't cite your sales enablement expertise.
03
The 'Brand Moat' in AEO for SDRs is 'Co-occurrence'. You want search models to associate '[Your Brand]' with phrases like 'The most effective SDR prospecting tool' or 'best SDR training resources'.
04
Declarative Truths win for SDRs. AI models favor objective, verifiable facts about sales development over subjective marketing claims. Shift your SDR enablement copy from 'Marketing' to 'Education' and 'Data'.
