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AI Citation Audit: Does the Source Really Support the Answer?
Run an AI citation audit to see whether linked sources really support each claim. Split claims, read the passages and label the evidence.
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AI Retrieval
Practical AI Retrieval tutorials, workflows, and experiments from SEOVision.
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Hallucinated Links in AI Answers: Diagnose and Fix Broken URLs
Hallucinated links in AI answers can point to pages that never existed. Find the origin, triage each URL and redirect only to verified equivalents.
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Learn how to update outdated content for real: diagnose what changed, choose the right action, build an evidence-based queue and finish every fix.
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Create a Consistent Product Identity Across the Web
Create a consistent product fact sheet across your website and third-party profiles so buyers can verify names, features and availability.
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Write Sections That Still Make Sense When AI Quotes Them
Write self-contained content sections that remain understandable when quoted, with clear subjects, conditions and evidence near the claim.
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Add Structured Data for Clear Facts, Then Test AI Outcomes
Use structured data to describe verified page facts, then test AI citation outcomes separately without assuming markup guarantees visibility.
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How to Test llms.txt Usage on Your Website
Measure llms.txt usage with server logs: set up a clean test, label your own traffic, read a zero correctly and report access separately from citations.
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Separate AI Crawlers, Search Bots and User Fetches
Create an AI crawler access policy by distinguishing crawler purpose, verifying current documentation and checking what your server actually receives.
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Use Query Fan-Out to Find Missing Buyer Information
Use query fan-out as a planning aid to uncover missing buyer questions without treating AI-generated subqueries as real search demand.
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Training, Retrieval and Tools: Diagnose an AI Answer’s Source
Distinguish model knowledge, retrieved pages and live tool results when diagnosing where an AI answer may have obtained a product fact.