/terms/e-e-a-t-ai-search

E-E-A-T (AI search context)

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's content quality framework, repurposed in 2024–2026 as a signal AI engines weight when deciding which sources to cite.

Citation status

ChatGPTPerplexityClaudeCopilot

Last checked 2026-05-21

What is E-E-A-T in the AI-search context?

Originally part of Google's Search Quality Rater Guidelines — Experience was added to E-A-T in December 2022 — E-E-A-T was a human-rater framework, not a direct ranking signal. By 2025, AI engines started inferring E-E-A-T signals from structured content: author bylines and credentials, organization schema, citation density, and freshness metadata.

Status in 2026

Standardly invoked but never publicly weighted. Most AI engines decline to confirm whether E-E-A-T flows into their citation ranking. Yet content with strong E-E-A-T signals (verified authors, sourced claims, regular "Last updated" markers) is cited at meaningfully higher rates per third-party measurements.

How it relates to other concepts

FAQ

Do AI engines explicitly use E-E-A-T?
They don't publicly disclose the weights. But content marked with author schema, organization details, sourced claims, and recency signals tends to be cited at meaningfully higher rates across ChatGPT, Perplexity, and AI Overview.
Is the new 'Experience' E the most important signal?
For YMYL topics (your money, your life — health, finance, legal), Experience matters most. For technical and B2B topics, Expertise and Authoritativeness continue to dominate.
How do I signal E-E-A-T to AI engines?
Author schema with credentials, organization details, sourced claims (every non-obvious fact linked to a source), datePublished and dateModified frontmatter, and a visible 'About' page describing the publication's editorial process.

Sources & further reading