Google's E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) guidelines were written for human quality raters but have directly influenced how LLMs are fine-tuned to weight sources. Content attributed to a named, credentialed author consistently outperforms anonymous content in AI retrieval studies — often by a factor of two or more.
What Counts as an Author Expertise Signal
The strongest signals are: a named author with a structured biography; links from the author bio to a verified LinkedIn profile, institutional page, or byline archive; published works in recognised outlets; and explicit mention of relevant credentials ("10-year Reuters correspondent," "PhD in computational linguistics"). These signals are picked up by both Google's quality raters and LLM fine-tuning processes that reward trustworthy content.
Structuring Author Bios for Maximum Signal
Use Person schema (JSON-LD) for every author. Include name, job title, affiliation, description, and a link to a verified external profile. Place a visible author box below every article with a photograph, short bio, and link to the author's article archive. These structural elements make it trivial for LLMs to identify and weight the author's expertise.
Institutional Affiliation Matters
An author affiliated with a named news organisation, university, or think tank inherits the domain authority of that institution. If a byline reads "Sarah Chen, AI Correspondent, Reuters" rather than just "Sarah Chen," retrieval systems automatically elevate that source's credibility. Even for independent journalists, formal affiliations (a membership organisation, a university adjunct position) can boost attribution confidence.