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