GEO content quality and E-E-A-T assessment

Purpose

AI search platforms do not just find content — they evaluate whether content deserves to be cited. The primary framework for this evaluation is E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), which per Google's December 2025 Quality Rater Guidelines update now applies to ALL competitive queries, not just YMYL topics. Content that scores high on E-E-A-T is dramatically more likely to be cited by AI platforms.

Two lenses:

1. E-E-A-T signals — does the content demonstrate real expertise and trust?
2. AI citability — is the content structured so AI platforms can extract and cite specific claims?

Operational protocol

1. Fetch the target page(s) — homepage, key blog posts, service/product pages
2. Evaluate E-E-A-T across the 4 dimensions (25 points each) using rubrics in signals.md
3. Assess content quality metrics (word count, readability, paragraph/heading structure, internal linking) using scoring.md
4. Check for low-quality AI content signals (see signals.md)
5. Evaluate content freshness and topical authority modifier (scoring.md)
6. Score and generate GEO-CONTENT-ANALYSIS.md using the template in templates.md

References

• signals.md — per-signal scoring rubrics for all 4 E-E-A-T dimensions, plus AI content quality signals (low/high)
• scoring.md — word count benchmarks, readability, paragraph/heading/linking rules, freshness scoring, topical authority modifier
• templates.md — GEO-CONTENT-ANALYSIS.md output template

Final scoring (0-100)

Interpretation

• 85-100: Exceptional — strong AI citation candidate across platforms
• 70-84: Good — solid foundation, specific improvements will increase citability
• 55-69: Average — multiple E-E-A-T gaps reducing AI visibility
• 40-54: Below Average — significant content quality and trust issues
• 0-39: Poor — fundamental content strategy overhaul needed