Most pages are written as arguments. They open with context, build a case across several paragraphs, and arrive at a conclusion somewhere near the bottom. That structure works for a human reader who has already decided to spend four minutes with you. It fails completely when a language model is scanning for a passage it can lift and attribute.
When an assistant answers a question, it is looking for a self-contained block of text that resolves the question without needing the surrounding page. If your answer only makes sense after three paragraphs of setup, it is not a candidate. The model will find a thinner page that stated the answer plainly in two sentences and quote that one instead.
The fix is structural rather than editorial. Put a direct answer immediately beneath every question-shaped heading, in forty to eighty words, written so it survives being cut out of the page entirely. Then continue with the depth, the caveats, and the evidence. You lose nothing with human readers, who scan for the same thing, and you become quotable.
An assistant will not summarize a page it has to interpret. It quotes the page that already did the work.
We rebuilt one client’s service pages on exactly this pattern without adding a single new page or link. Assistant citations went from zero to appearing in roughly a third of the tracked prompts within nine weeks. The rankings barely moved. The extractability did all the work.
Test your own pages the honest way. Copy a single paragraph out of context and read it cold. If you cannot tell what question it answers, no model can either.
