Laatst bijgewerkt: 30 april 2026
Expert Take: Query fan-out shows which sub-questions Google and AI systems derive from a search intent, and E-E-A-T helps determine which content answers those sub-questions most reliably and usefully. If your content scores well on E-E-A-T for each sub-question, you increase the chance that Google sees your page as a source that can cover multiple parts of the information need.
Let’s get the basics right first.
A good recruiter doesn’t decide based on the interview alone. The real selection happens at the reference check: what do external sources say about this person, independent of the story they tell themselves? AI models work the same way. Over the past few weeks I’ve used Quolity’s Chrome extension to collect hundreds of AI runs and log thousands of fan-out queries: the follow-up searches a model performs after a user asks an initial question. Not the main prompt itself, but the search path that unfolds after it.
Consistent pattern: brands become visible or invisible in that second layer — in the question of which external sources, experts and validation signals the model looks up alongside the answer. That maps precisely onto what Google means by E-E-A-T, which is why I’m connecting the two. Not as a ranking formula, but as a diagnostic framework: is the model searching for practical examples? Cited experts? Independent comparisons? My hypothesis: fan-out queries reveal what kind of evidence a model needs to find a brand credible enough to cite.
That makes the question more concrete than “how do I rank in AI?”: which search path does this question open, what evidence does the model expect along the way, and where does my brand still lack the trust, expertise, experience or authority to become visible? Over the coming period I’ll be ‘reporting’ on these measurements on this website — outcomes, methodology, patterns and limitations. The foundation has to be right first.