Before I build a page, I write down who lands there and the question they bring. Working out what a searcher needs before building covers that half. After the page ships, I compare its Google queries with that sentence. Bing's site-wide AI searches add context about the questions its answers researched. This post is about reading their version against mine. It's the second outcome in my system, get understood, checked after launch instead of planned before it.
Architecture, entity clarity, structured data. The signals that tell search what a page is. Those are what I build. The searches are how I find out what search actually took from them.
What do I compare once a page is live?
Three things, side by side:
- What the page is for. One sentence: who searches, and what they need from this page. On this site that sentence lives in a file, and the build fails any page that doesn't have one.
- The searches Google shows it for. Search Console's queries, filtered to the page, with the position for each.
- The searches Bing's AI ran before citing the site, from Bing's AI Performance report, next to the pages it cited. Search intent when AI does the searching explains what those searches are and where Bing's intent labels come from.
One limit shapes everything below. Bing's search export and its page export are separate reports, so I can't join one AI search to the page it cited. I read a site's searches next to its cited pages and look for where they agree.
What does a match look like?
GageRef is my reference for welders and fitters. The most-cited search in its Bing sample is the code printed on a spool of aluminum filler wire, typed with nothing around it. Bing's intent label for it is "Others". That search drew 1.2K citations, first of the 159 in the export. Separately, GageRef's page named for that same wire drew 1.3K citations, sixth of its 96 cited pages. The two reports don't join, so this is a match in what they name, without proof that one fed the other. (Bing Webmaster Tools, AI Performance, April 13 to October 11, 2026, exported October 13.)
GageRef also taught me the other side of it. Welders search by brand name more than I expected. I had to retrofit brand names and manufacturer equivalents into existing pages after seeing the data. The live site now has a brand cross-reference covering Lincoln, ESAB, Hobart and Forney. The pages use the words on the spool and on the box, because those are the words in the search. The GageRef case study has the rest, including a fittings side that went live too thin.
What does it look like when they disagree?
RatingPlate is my clearest case. I wrote its job as homeowners pricing a heat pump water heater in their state, and built state cost guides for it. Bing's AI read it as a label decoder instead: every search in its sample is about identifying a water heater, and the rating plate guide drew most of the citations.
When the searches disagree with my sentence, I check the sentence first. Here the engine had found a job the site does better than the one I wrote down, so I followed it and rebuilt the guide in July 2026. The RatingPlate case study has the counts, the rebuild and what I still can't attribute to it.
Do Google and Bing read a site the same way?
Title Town PDX, my site for Portland fans of the Fire, the Thorns and the Cascade, gets two different readings.
Google's AI Overviews and AI Mode showed its three game-day guides more than any other page: the Providence Park guide 2K times, Moda Center 1.2K and Hillsboro Ballpark 1.1K, from July 12 to October 11, 2026. Bing's AI cited the Fire roster most, 8.3K of the 20.8K citations it tied to a page (40%), while those three guides together drew 1K (about 5%), from April 13 to October 11. Google's regular results lean the same way as its AI: Title Town's top search by clicks in the four weeks to October 23 asked about a stadium's bag policy, and it showed up 417 times at an average position of 9.5. (Search Console's Generative AI report and its search results report, data through October 11 and October 23; Bing Webmaster Tools, AI Performance, exported October 13.)
These are different counts over different windows, so neither the sizes nor the rank order is a controlled comparison. In these exports, game-day guides lead Google's AI list and a roster leads Bing's. Both fit jobs I built the site to do. Those rows alone give me no mismatch to fix; I still inspect the pages and their next steps before deciding what to change. Each engine gets its own column, and a change made for one gets checked in both.
What did reading after launch catch?
On WireRef, my electrical code reference, where a wrong reading is a safety problem, the searches raised a question about scope rather than a wrong number.
WireRef's sample holds 51 searches about 200 amps. 27 of them say "service". The other 24 don't, and the sixth most-cited search in the whole export, 1.2K citations, is the number 200 followed by the letter A, nothing else, with no word on whether it means a circuit or a service. (Bing Webmaster Tools, AI Performance, April 13 to October 11, 2026.) A page that gets that search has to say which question it's answering, inside the answer itself. A matching search still can't tell me the answer is right; the WireRef case study has the checks that do.
What do I change when they disagree?
Find the row that matches what the searches show.
| What the searches show | What I change | Where I did it |
|---|---|---|
| The engine picked a job the page does better than the one I wrote | Rewrite the page's sentence and build that job up | RatingPlate's guide, rebuilt in July 2026 |
| People search in words the page doesn't use | Put their words in the headings and the first lines | GageRef's brand names |
| One search can mean two questions | Say which one, inside the answer passage | WireRef's service sizes |
| A different page of mine shows up for the question | Link from that page to the right one, with the question's words in the link | What internal links tell crawlers |
| One page shows up for two jobs with different next steps | Split it; keep one page when the next step is the same | Which questions deserve their own page |
| Each engine reads the site its own way | Read each one separately, and change nothing just to please one | Title Town PDX |
Where the answer passage goes, and how long it runs, is in how to structure content for answer-first search. At my full-time job the same reading decides what goes into an FAQ: questions come from search queries, People Also Ask and support conversations, and a question nobody asks stays off the page. How I work at my full-time job has that play.
What can't this check tell you?
- Which page answered which AI search. WireRef's third most-cited search asks what low-voltage wiring is best for home security cameras, a shopping question, with 2K citations. WireRef is a code reference, and the export won't say which of its pages Bing used for it.
- Google's AI searches. Search Console's Generative AI report, as I export it, lists pages, countries and devices. It doesn't list the searches, so only Bing shows what its AI searched for.
- Every search. Google says Search Console leaves some queries out, so a rare phrasing can be missing from the list.
- Whether a label is right. Bing's intents come from a classifier. GageRef's top search got "Others", and the intent post shows labels that miss outright.
- Whether a person understood the page. Searches show what the engine took from it. Whether a reader can use it takes a reader, which is the last step of the page brief. How AI search finds and understands your content covers what the page needs for the engine's half.
It's the same habit I keep for decisions: write down what you're doing and why before you know if it works. Then come back and see if the reasoning held up. The page's sentence is the reasoning. The searches are how I come back to it.
Written for this site and first published October 30, 2026. Every number in it comes from my own exports and is dated where it appears.