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                "text": "Module 7: Citation Eligibility Engineering"
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        "markdown": "---\ntitle: Citation Eligibility Engineering | NRLC.ai\ndescription: Citation Eligibility Engineering on NRLC.ai. AI SEO services, structured data engineering, and LLM optimization solutions. Neural Command, LLC builds AI retrieval and...\norganization: Neural Command LLC\ncanonical: https://nrlc.ai/en-us/docs/prechunking-seo/course/citation-eligibility-engineering/\n---\n\n← Back to Course Overview | ← Previous: Module 6\n\nWhy AI avoids citing most content.\n\nLLMs apply citation filters to avoid citing content that sounds promotional, makes guarantees, lacks scope, or mixes opinion and fact. Related: Failure Modes\n\nLLMs avoid citing content that:\n\n- sounds promotional\n- makes guarantees\n- lacks scope\n- mixes opinion and fact\nCitation is a trust signal. LLMs only cite content they can verify and defend.\n\nWrite chunks that are:\n\n- factual\n- scoped\n- boring\n- safe\nBoring content gets cited.\n\nRemove marketing language, superlatives, and emotional framing. Write declarative facts.\n\nTask: Take a promotional paragraph from your content. Rewrite it as a factual, scoped, boring chunk that is safe to cite.\n\nConstraint: Remove all marketing language, superlatives, emotional framing, and guarantees. Keep only declarative facts with explicit scope.\n\nWhat success looks like: You produce a chunk that sounds like a reference manual entry, not a sales page. An LLM reading this chunk would feel confident citing it because it's factual, scoped, and safe.\n\nThis task is optional. No submission required. No validation. Use it to convert theory into applied thinking.\n\nNext: Module 8: Measuring Prechunking Success\n\n\n\n---\n\nSource: https://nrlc.ai/en-us/docs/prechunking-seo/course/citation-eligibility-engineering/\nPublisher: Neural Command LLC\nLicense: Editorial use with attribution\n"
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