Bing Webmaster Tools AI performance report
How to use grounding queries as an editorial brief

What makes grounding queries more useful than standard keyword reports?

Standard search query reports tell you what people typed before a click or impression in classic search. Grounding queries tell you something different. They show the language patterns an AI answer system used when selecting sources and building a response. That is closer to citation mechanics than normal SEO reporting, which is why this report matters.

In practice, grounding queries help you see whether your page is being matched to comparison intent, definition intent, process intent, troubleshooting intent, or brand validation intent. That is editorial gold. You can rewrite pages around the exact kinds of questions models appear to ask internally, instead of guessing from a generic keyword tool.

This matters even more because AI crawlers are still operationally simple in one critical way. Verified server log research from late 2024 showed AI crawlers fetch JavaScript files and never run them. If your most quotable answer is hidden behind client side rendering, the grounding query report may expose the symptom before your team notices the cause. You see prompts that should match you, but weak visibility on the page most likely to answer them.

How should you actually read the Bing AI performance report?

Do not open the report looking for vanity wins. Open it looking for repeated language patterns. The useful unit is not one prompt. It is the cluster.

• Group grounding queries by intent, not by exact wording.
• Map each cluster to one page that should be the best answer.
• Check whether the answer is visible in raw HTML, near the top, and stated plainly.
• Compare the query language to your headings, summary paragraphs, lists, and tables.
• Look for clusters where Bing keeps grounding on a weaker page instead of your intended page.
• Create missing pages only when a cluster keeps appearing and your current page cannot be fixed without becoming unfocused.

What you are trying to detect is mismatch. Sometimes the mismatch is topical. Sometimes it is structural. A page may discuss the right concept but bury the answer under scene setting, product framing, or design elements that are obvious to a human and invisible to a non rendering crawler.

I would also watch for grounding queries that sound more specific than your page copy. That usually means the model can kind of use your page, but it is filling in the exact framing from elsewhere. When that happens, your page may be eligible for inclusion but not memorable enough to be consistently cited.

The simple reading model I use

• If the query cluster exists and you have no focused page, that is a coverage gap.
• If the cluster exists and the right page exists but is not plain enough, that is an extractability gap.
• If the cluster exists and the wrong page is being used, that is an internal relevance and architecture gap.
• If the cluster exists but the traffic signal is thin, that is a patience problem, not always a content problem.

Which page changes usually improve grounding query alignment?

Most gains come from boring fixes. Better first paragraphs. Cleaner headings. Tighter entity naming. More explicit comparisons. Short lists that state trade offs directly. Tables when a user is clearly evaluating options or categories. None of this is glamorous. It is just easier for machines to lift and cite.

The first rewrite pass should focus on answer placement. Put the direct answer early. Then support it with scope, conditions, and exceptions. If your page only becomes useful halfway down, you are asking too much of the retrieval layer.

The second pass should focus on disambiguation. Say exactly what a tool, method, or entity is. Name close alternatives. Explain where your claim fails. Honest limitations are not just good editorial practice. They often make the extracted answer safer to use.

The third pass should focus on structure. When a page naturally supports comparison, use a table. When it supports procedures, use ordered subheads and short steps. When it supports definitions, lead with the definition and then expand. A lot of teams chase exotic schema before fixing the sentence that should have been quotable in the first place.

Where does this report mislead teams?

First, it is a Bing and Microsoft view, not the whole AI search market. That does not make it useless. It makes it partial. If your audience heavily uses other assistants, you cannot assume the same prompt patterns, retrieval behavior, or citation outcomes everywhere.

Second, grounding queries can tempt teams into overfitting content to surface phrasing. That is a mistake. You should align to underlying intent and answer format, not produce awkward copy that parrots query wording. Models change. Good explanations last longer than prompt mimicry.

Third, this report cannot prove business impact on its own. A page may be used for grounding because it is factual and concise, while another page is the one that actually converts. Do not let the content team optimize exclusively for citation eligibility if the commercial path depends on a different asset.

Fourth, some teams treat every AI visibility tactic as equally validated. It is not. A good example is llms.txt. Google has stated it is not used by Search. A large study across about 300,000 domains found 10.13% adoption, none among the top 1,000 sites, and no citation lift after controls. So if your team is debating whether to polish grounding query alignment or spend the week on llms.txt theater, choose the former.

Who should not follow this advice too aggressively?

If your site has basic rendering, crawlability, or canonical issues, do not jump straight into grounding query copy edits. Fix the foundation first. Editorial polish cannot rescue a page that is hard to fetch, hard to parse, or split across conflicting URLs.

If you publish original research, technical documentation, or regulated content, do not over compress nuance just to make snippets easier to extract. The goal is better retrieval without making the answer less accurate. In some categories, a more cautious answer is the right answer.

If you want outbound execution, this is not the site for that playbook. We run managed outbound under Outbound Pros, and that team handles prospecting and channel execution there. Here, I care about whether your pages can be found, parsed, and cited by AI systems.

What is the practical workflow for turning grounding queries into content decisions?

I would run this weekly for active sites and monthly for slower publishing teams. Pull the report. Cluster the grounding queries. Map each cluster to a page. Review whether the page answers the query in plain language inside the visible HTML. Then decide whether to rewrite, consolidate, or publish.

• Export or copy the grounding queries into intent clusters.
• Label each cluster as definition, comparison, workflow, troubleshooting, or validation.
• Assign one preferred page per cluster.
• Check whether the answer appears high on the page and in server rendered HTML.
• Add short answer blocks, lists, and tables where the topic naturally needs them.
• Remove throat clearing and product fluff from the first screen of copy.
• Recheck later for whether the same clusters keep appearing with better page alignment.

That workflow is not perfect, but it is grounded in observable behavior. It keeps the team focused on what a machine can retrieve and quote, instead of what a brainstorming doc says users might ask.

The bigger point is simple. Grounding queries are useful because they expose the bridge between prompts and pages. That bridge is where most AI visibility work actually lives. Not in mythology. Not in unsourced GEO multipliers. In whether your page cleanly answers the kind of question a retrieval system is trying to satisfy.