Query fan-out: why one question hits many of your pages
Google documents query fan-out in AI Overviews and AI Mode: one question becomes many searches. What it means for sub-questions, page scope and internal links.
16 posts
Google documents query fan-out in AI Overviews and AI Mode: one question becomes many searches. What it means for sub-questions, page scope and internal links.
Documentation is already question-shaped. Task titles, a prose answer before the code, versioned URLs with clean canonicals, and where llms.txt fits.
The page-side half of GEO: citable sentences, authorship, entity clarity, honest dates, trust surfaces, and linking out, mapped to signals you can check.
Main-content extraction keeps one block of your page and drops the rest. How to measure what survives, by hand and across a site, and the markup that helps.
Is llms.txt worth it? The proposal, the file format with a worked example, what is documented about who reads it, and when the afternoon pays off.
Four positions cover almost every site. The copy-paste file for each, the matching rules that quietly break them, and the maintenance the file needs.
The rich result that justified most FAQ blocks is gone. What still earns one, the test for whether a question belongs in the body, and what filler costs.
Real markup is only half of it. What flattening does to a table lifted out of your page, and the four fixes that keep one readable on its own.
A page is retrieved in pieces of a few hundred words. What splitting does to ordinary prose, and the three edits that make a section survive it.
An element-by-element inventory of what AI crawlers keep, discard, and only conditionally read — plus the parts you can exclude on purpose.
What happens to a page between fetch and answer, the eight signals answer-readiness turns on, and how to check every one of them by hand and at scale.
Three acronyms, one page. Here is what answer engines and generative search changed about optimization, and what they did not.