How to Use Bing’s AI Citation Reports to Find Content Gaps

Imagine maintaining documentation for a webhook API. Your quick-start guide covers authentication, payloads, and a successful request. It looks complete.

Then you review AI citation activity and notice grounding phrases about retries and duplicate events.

Your documentation mentions both, but never explains what happens when a request times out after your application has already processed it.

That is a useful content gap. Another introduction to webhooks will not fix it. A tested explanation of retry behavior might.

This is a hypothetical example, but it captures a practical way to use Bing’s AI citation reports: turn observed retrieval activity into questions your documentation needs to answer better.

For developers, technical writers, and teams maintaining product content, the interesting output is a better backlog.

The feature is called AI Performance in Bing Webmaster Tools. Microsoft introduced it in public preview in February 2026, covering Microsoft Copilot, AI-generated Bing summaries, and selected partner integrations.

Grounding queries are retrieval phrases, not necessarily the original prompts users typed. Citation counts do not indicate position within an answer or measure visits. The report covers supported experiences, rather than every AI platform.

Microsoft describes these capabilities in its AI Performance announcement.

Treat the data as evidence of observed use. It is not a complete inventory of questions your audience asks.

Choose a verified site in Bing Webmaster Tools, open AI Performance, and select a reporting period. Begin with one area you can realistically improve: authentication, deployment, billing, or a particular integration.

Record the cited URLs, their counts, the reporting dates, and relevant grounding phrases available in the interface. A spreadsheet or local CSV is enough.

Preserve the relationships the report actually provides. If a query-to-URL relationship is unavailable in your view, label your proposed mapping as an inference. Do not join two separate lists and present the result as observed data.

For the webhook example, your audit might include:

These are illustrative entries, not actual Bing report data.

The investigation column is what makes this useful. “Improve webhook SEO” is a vague task. “Explain duplicate delivery after a timeout” is something an engineer can review.

A page with few citations can still contain an excellent answer. Before rewriting it, inspect the page and check whether it is indexed and accessible.

I would classify potential gaps into four groups.

The site does not explain a relevant task.

Your quick start demonstrates receiving an event, but says nothing about handling repeated deliveries. Search the documentation, support knowledge base, repository examples, and issue tracker before declaring the answer missing.

An existing answer may need consolidation rather than another URL.

The topic appears, but the explanation does not help someone implement it.

“Make your handler idempotent” names a requirement. It leaves the developer to work out event identity, storage, transaction boundaries, and recovery after failure.

The fix could be a worked example with explicit assumptions. That is much more valuable than repeating the term “idempotency” across several headings.

The page makes a claim without enough context to trust or reproduce it.

“Supports large payloads” is ambiguous. State the documented limit, applicable API version, and behavior when that limit is exceeded.

For benchmarks, include the environment and method. For integration examples, name the versions you tested. For product behavior, confirm the answer with the team responsible for it.

The answer exists, but you find a problem with how it is delivered or connected.

Check response status, indexing directives, canonical URLs, rendered content, and links from related documentation. Inspect public responses and Bing’s inspection tools rather than assuming that a working browser session proves crawler access.

Microsoft’s guidance for inclusion in AI answers recommends crawlability, clear structure, precise wording, and self-contained answers. Those are useful foundations, without a guarantee of citation.

A grounding phrase gives you a starting point. Your audit should investigate the task it suggests.

For “duplicate webhook events,” read the candidate page and ask:

Verify these details against your actual product. Different systems have different delivery guarantees and identifiers.

Keep related phrases together when they describe the same task. “Webhook duplicate delivery” and “deduplicate webhook events” may need one strong section rather than two near-identical articles.

That approach also helps you build connected topic coverage without creating a separate page for every wording variation.

Developer audiences do not need another vague content score. They need a ticket that explains what to change and how to verify it.

Here is an illustrative issue template you can adapt:

title: Explain duplicate webhook delivery page: /docs/webhooks/delivery evidence: report: Bing Webmaster Tools AI Performance reporting_period: "Record the dates reviewed" grounding_phrase: "Record the phrase observed" page_mapping: "Inferred; confirm during audit" gap: type: answer finding: "Current guide mentions retries but omits duplicate handling" proposed_change: - Explain the product's documented delivery guarantees - Identify the event ID used for deduplication - Add a tested example for concurrent deliveries acceptance_criteria: - Product owner verifies behavior and terminology - Engineer tests the example against the named version - Relevant guides link to the revised section This is your own backlog format, not Bing’s export schema.

Prioritize issues using three questions: does the task matter to your users, is the gap confirmed, and can you fix it with reliable evidence?

A frequently cited introductory page may be less urgent than a deployment guide that leaves developers unable to finish an integration.

If the page already serves the task, strengthen it there.

Replace a heading like “Advanced considerations” with “How to handle duplicate webhook deliveries.” Start with the verified recommendation, explain its assumptions, and follow with the example.

Keep enough context for the answer to make sense on its own. A paragraph beginning “This also applies here” forces readers to reconstruct the preceding section.

Create a separate page when the task needs substantial independent treatment. Link it from the quick start and relevant reference pages so developers can reach it during the workflow.

The acceptance test is straightforward: can someone complete the task using this documentation?

Record the publication date and scope of each update. Compare consistent reporting windows and, where practical, retain a similar unchanged page as context.

Choose usefulness measures your team can actually observe. A documentation team might care more about fewer repeated support questions than more page views.

Keeping visibility, traffic, and conversions separate prevents one improving metric from becoming a claim about everything else.

More citations after a rewrite are an observation. Changes in topic demand, report coverage, or other site updates could also contribute.

If your team separately experiments with targeted search traffic, SearchSEO’s Bing campaigns offer keyword and geographic targeting. Record campaign periods and generated activity separately from your content audit. Running a traffic campaign during the same window makes it harder to attribute changes in visits.

There is no established causal link here between buying search clicks and earning AI citations.

Bing’s AI citation reports can help you investigate where your content supports an answer and where the surrounding task needs more work.

Start with one documentation area. Review the observed phrases, verify the gaps, and ship a small set of changes an engineer can test.

If you also want to explore a separate search-traffic experiment, review SearchSEO’s Bing offering with its own hypothesis and measurement plan.

For the content work, the next useful result is a developer who can finish the integration without opening another support ticket.