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# E-E-A-T Evaluation Framework
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Use this rubric to evaluate content quality for competitive queries.
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## Dimensions
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Score each dimension from 0 to 5:
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| Dimension | What to Check |
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|-----------|---------------|
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| Experience | First-hand evidence, practical examples, real usage context |
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| Expertise | Subject competence, technical accuracy, depth, specialist language used correctly |
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| Authoritativeness | Reputation signals, citations, recognized brand/person entities |
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| Trustworthiness | Accuracy, transparency, policy clarity, editorial rigor, safety disclosures |
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## Query Stakes Classification
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Classify intent before scoring:
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- YMYL-high: finance, health, legal, safety
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- Commercial-high: high-ticket buying decisions
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- Informational-competitive: dense SERP competition with strong entities
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- Informational-low: low-risk, basic educational intent
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Raise trust requirements for higher-stakes intents.
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## Evidence Checklist
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Review per template:
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1. Authorship clarity:
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- named author
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- credentials or role
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- updated timestamp
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2. Source quality:
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- primary sources cited
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- external corroboration
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- quote/context integrity
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3. Editorial quality:
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- original analysis (not paraphrased commodity text)
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- internal consistency across pages
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- clear claims with evidence
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4. Trust assets:
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- about page and editorial policy
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- contact and business identity
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- privacy and terms links
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5. User value:
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- intent match
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- completeness
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- actionable depth
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## Risk Flags
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Flag as high risk when you find:
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- anonymous or credential-free expert claims on high-stakes pages
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- copied or near-duplicate boilerplate across key landing pages
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- medical/financial/legal recommendations without sourcing
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- exaggerated claims without verifiable proof
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## Scoring Output Format
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Return:
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1. Overall content quality score (0-100)
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2. Per-dimension scores mapped from rubric
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3. Top 3 trust risks
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4. Fastest high-impact remediation actions
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## Remediation Patterns
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- Add expert-reviewed blocks with named reviewers on high-stakes pages.
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- Replace generic intros with first-hand evidence and data-backed claims.
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- Add citation discipline: primary sources first, then secondary context.
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- Standardize author bylines, update dates, and revision ownership.
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