Insights · E-E-A-T and Trust

E-E-A-T and LLMs: a factual editorial framework

Google uses E-E-A-T in its quality-evaluation guidance. Other answer engines do not expose an equivalent score. This guide uses the four dimensions as an editorial checklist while keeping product-specific claims separate.

Updated 19 September 2026 12 min read

Why E-E-A-T concerns LLMs

E-E-A-T is an evaluation framework published by Google in its Search Quality Rater Guidelines. The four letters stand for Experience (direct experience of the author), Expertise (competence on the subject), Authoritativeness (external recognition) and Trustworthiness (overall source reliability).

External answer engines do not consult a public Google E-E-A-T score, and their providers do not publish evidence that they reproduce Google's evaluation framework. It is therefore inaccurate to turn E-E-A-T into a cross-platform ranking formula.

The practical use is editorial: ask whether a reader can identify who produced the content, what direct experience supports it, which sources establish the facts and when the page was last substantively reviewed. Google's people-first content guidance uses similar self-assessment questions without promising a ranking outcome.

Dimension 1 - Experience (direct experience)

Experience refers to proof that the author or organisation has actually lived, tested or observed what they are talking about. For LLMs, this signal manifests primarily through two textual patterns:

Original data and field observations

Internally produced data can demonstrate direct experience when the sample, period, method and limitations are disclosed. A large number alone proves nothing. A small reproducible test can be more useful than an unexplained dataset.

Operational vocabulary and implementation details

Real experience shows through practical details: errors encountered, edge cases, actual execution times, tested variants. These details help readers distinguish first-hand work from a summary. No public documentation establishes a universal answer-engine weighting for this pattern.

Concrete actions:

Dimension 2 - Expertise (competence on the subject)

Expertise concerns the depth of competence on a specific domain. Review it through evidence a reader can inspect:

Depth and terminological consistency

An expert uses the precise vocabulary of their domain consistently. They distinguish web RAG from memorised RAG, fixed chunking from structured chunking, AI Overview from SGE. They do not use terms interchangeably. This terminological precision reduces ambiguity for readers and reviewers.

References to primary sources

Expert content cites original studies rather than a chain of summaries. A direct link lets readers inspect the method, sample and limitations instead of trusting an unattributed claim.

Topical authority: breadth and depth of coverage

An expert site covers its domain in depth: not only the main question, but adjacent questions, edge cases, history and controversies. Internal links and clear coverage help users navigate related questions. Do not treat "topical authority" as a documented citation score for external answer engines.

Concrete actions:

Dimension 3 - Authoritativeness (recognised authority)

Authoritativeness asks whether independent, relevant sources recognise the author or organisation. The evidence should be verifiable without claiming access to a model's training data.

Co-occurrence in reference corpora

Independent press, public records and sector reports can help readers verify an organisation's identity and claims. You generally cannot prove that a particular reference entered a model's training data or caused a later answer.

Thematic quality backlinks

Relevant inbound links can support discovery and conventional search visibility. Do not promise that a link will enter future training data or alter an answer engine's source selection.

Sector canonical sources

A genuine inclusion in a maintained sector directory can provide independent evidence. Paid, self-created or outdated lists should not be presented as authority.

Concrete actions:

Dimension 4 - Trustworthiness (overall reliability)

Trustworthiness is the most transverse dimension - it conditions the weight given to the other three. Reliable content for an LLM presents several characteristics:

Rigorous dating

Dates help readers judge whether a claim may be stale. Show a modification date only after a substantive change and date time-sensitive facts at the claim level.

Explicit and verifiable sources

Link to the studies, reports or official pages that establish a claim. This is good editorial practice and makes checking possible; it is not a documented cross-platform ranking signal.

Organization schema with sameAs

Organization markup can describe an organisation and link to profiles that genuinely represent the same entity. Keep it consistent with visible content. No provider documents it as a direct trust or citation score.

Cross-source consistency

If your site says you were founded in 2019, but another official profile says 2020, readers cannot know which value is correct. Resolve discrepancies on sources you control and document the evidence.

Concrete actions:

Summary table: E-E-A-T by AI surface

Dimension Evidence to inspect Common failure Priority action
Experience Dated tests, method and limitations Claims of experience without reproducible detail
Expertise Precise terminology and primary sources Confident summaries that omit source limits
Authoritativeness Relevant independent references Self-created profiles presented as recognition
Trustworthiness Authorship, dates, corrections and source support Conflicting facts or cosmetic date changes

Four-priority action plan

Week 1 - Factual consistency audit

List all public sources where your entity is mentioned (site, LinkedIn, Crunchbase, Wikidata, Wikipedia, Google Business Profile). Create a table with key fields: name, founding date, sector, products, location. Correct inconsistencies on all sources you control.

Week 2 - Enriched Organization schema

Implement or enrich your Organization schema with foundingDate, numberOfEmployees, sameAs (minimum: Wikidata, LinkedIn, official Twitter/X), knowsAbout and description. Verify the rendering in the Google Rich Results Test.

Week 3 - Independent entity references

Review independent profiles and records that already cover the entity. Correct only sources you control. Create a Wikidata item only when the entity meets Wikidata's notability and sourcing rules; do not create one as a ranking tactic.

Week 4 - First original data publication

Publish an article or page containing data you produced yourself. It can be a benchmark of 20 URLs, a 30-day tracking of your positions in Perplexity, or a manual analysis of 50 ChatGPT responses in your sector. What matters is that the method, sample, period and limitations are explicit enough to audit.

FAQ - E-E-A-T and AI answer engines

Do LLMs actually read E-E-A-T signals from Google?
No. E-E-A-T is part of Google's quality-evaluation vocabulary, not a public score exposed to other products. Use it as an editorial audit, not as a claimed answer-engine formula.
Which E-E-A-T dimension matters most to be cited by Perplexity?
Perplexity does not publish a weighting that maps source selection to the four E-E-A-T dimensions. Measure citations on a documented query sample instead.
Does Organization schema improve E-E-A-T for LLMs?
It can describe an entity when the markup matches visible facts. There is no documented direct E-E-A-T or citation gain from sameAs.
How do you measure E-E-A-T improvement as perceived by LLMs?
Track visibility, answer accuracy and citation support separately on a stable panel. Better accuracy is useful evidence, but it is not an E-E-A-T score.

E-E-A-T checklist for LLMs (8 points)

  1. Articles disclose the method, sample, period and limits of original data.
  2. Sector vocabulary is precise, consistent, and documented in a glossary.
  3. Primary sources (studies, official reports) are cited with direct links.
  4. The site covers the domain in depth (topical authority, content cluster).
  5. Factual consistency is audited and synchronised across all public profiles.
  6. Organization schema is implemented with sameAs pointing to Wikidata, LinkedIn and official networks.
  7. All pages display a visible publication date and last update date.
  8. Independent entity references are accurate and meet each platform's rules.

À lire ensuite

Entity SEO for LLMs Seven actions to disambiguate an entity and strengthen its presence in LLMs. Schema.org and LLMs Implement structured data without overclaiming its effects. LOOP methodology The framework that integrates E-E-A-T into a complete LLM optimisation approach. Measure AI visibility Defensible KPIs to track the evolution of your credibility as perceived by LLMs.