LLM SEOSeptember 12, 2025
E‑E‑A‑T for LLM SEO (2025): Proving Expertise to AI Systems
Apply E‑E‑A‑T to LLM SEO in 2025. Concrete tactics to demonstrate experience, expertise, authority, and trust so AI systems consistently cite your content.
E‑E‑A‑T for Models: What Actually Matters
Models evaluate content via patterns that proxy for experience, expertise, authority, and trust. Your goal is to make those signals explicit and machine-verifiable—on-site and off-site.
Experience
- • First-hand data, photos, logs, code samples
- • Case studies with before/after metrics
- • Author notes: failures and constraints
Expertise
- • Canonical definitions and frameworks
- • Comparisons, decision trees, troubleshooting
- • Citations to standards and datasets
Authority & Trust, Programmatically
- Authors: Add Person schema, bios, credentials, and sameAs links.
- Reviews: Host public methodology and raw data when possible.
- Changelog: Timestamp updates to critical pages; show versioning.
- Security: HTTPS, privacy policy, terms, and contact consistency.
Author & Source Architecture
Author Pages
Unique slugs, Person schema, expertise areas, publications, and social verification.
Evidence Blocks
Tables with metrics and sources; downloadable CSV/JSON for transparency.
Policy Surface
Prominent links to privacy, security, editorial standards, and contact info.
Operationalize E‑E‑A‑T for AI Systems
LLM Outrank helps you prove expertise programmatically and surface the right signals models rely on.