AI Search Source Credibility: Build a 32-Field Audit Ledger
Adapt an EACL 2026 study into a claim-source audit that preserves sample, user role, credibility rules, groundedness, adjudication and limitations.
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SEO Champion | Google News, Google Discover & AI Search Expert | Founder of MustHave.ai, Abdessalam.ai & SearchEngineAnswer.com
Experience: Google News, Google Discover, and AI Search specialist; founder of MustHave.ai, Abdessalam.ai, and SearchEngineAnswer.com.
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Adapt an EACL 2026 study into a claim-source audit that preserves sample, user role, credibility rules, groundedness, adjudication and limitations.
Audit what an AI answer actually uses from each cited source with a claim-passage ledger for contribution, fidelity, review, limits, and re-audit.
Evaluate plugin metadata with direct, indirect, and negative prompts; confusion counts; argument and safety checks; controlled revisions; and a reusable ledger.
Match AI-visibility scores to referral sessions by page, platform, date, coverage and lag before calculating correlation or making a traffic claim.
Compare AI visibility tools by prompt provenance, execution method, evidence access, metric definitions, platform coverage, cadence, and decision fit.
A critical 2026 survey separates retrieval, citation, absorption and downstream behavior—and finds the evidence narrower than most GEO claims.
Separate intervention-aligned ChatGPT referral movement from platform growth with treated and control definitions, first-party evidence, placebo checks, organic guardrails, and a reusable ledger.
A 2026 simulation shows how citation-seeking rewrites can degrade quality—and how to test AEO changes without sacrificing evidence or reader value.
Separate Cloudflare Gateway MCP policy from public crawler controls with a 24-request fixture and a 145,325-request origin-log field audit.
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