How to Find Relevant Prompts Using Bing Webmaster Tools AI Performance

Bing Webmaster Tools just became the first search engine to give publishers direct visibility into how AI systems use their content.

The AI Performance dashboard, launched in February 2026, shows citation data, grounding queries, and page-level performance. No other search engine provides this natively.

Unlike Google Search Console where you need to reinterpret traditional search metrics for AI insights, BWT now shows native AI data (based on Copilot). This is a fundamental shift: you can see exactly how Microsoft Copilot retrieves, cites, and uses your content in AI-generated responses.

This guide walks through everything you need to extract actionable insights from BWT's AI Performance dashboard, from understanding grounding queries to building a complete optimization workflow.

Why Bing Webmaster Tools Matters for AI Visibility

Microsoft Copilot is not just another chatbot.

It's integrated into Windows, Edge, Microsoft 365, and Bing Search, reaching hundreds of millions of users daily across surfaces they already use. When a user asks Copilot a question in Edge or within Word, it pulls answers from Bing's index.

BWT's AI Performance dashboard is the only tool that shows how AI systems actually consume your content at the backend level.

The AI Performance Dashboard: What You Get

The AI Performance dashboard provides five core metrics that map directly to how Copilot uses your content:

Important distinction: These metrics reflect citation frequency, not ranking, clicks, or traffic nor prompts. A page with high citations is frequently used as a source by Copilot, but that doesn't directly translate to referral traffic. Think of it as measuring your role as an information source in the AI ecosystem.

Grounding Queries: The AI's Internal Search Language

Grounding queries are the most valuable data in the AI Performance dashboard, and also the most misunderstood.

What they are: When a user asks Copilot a question, the system doesn't search Bing's index with the user's exact words. Instead, it reformulates the question into one or more internal queries optimized for retrieval. These reformulated queries are what BWT calls "grounding queries."

Example: A user asks Copilot "What's the best project management tool for remote teams?" Copilot might generate grounding queries like:

• project management software remote collaboration features comparison 2026
• best project management tools distributed teams reviews
• remote team productivity software pricing features

Notice the difference. The user's prompt is conversational and vague. The grounding queries are keyword-dense, specific, and optimized for Bing's index. They read like machine-generated retrieval queries because that's exactly what they are.

Why they matter: Grounding queries reveal how AI interprets user intent and what content it actually seeks. They show you the topics and entities your content is associated with in the AI's knowledge model. This is fundamentally different from traditional keyword data because it shows the retrieval layer, not the user-facing layer.

Practical value: If you see grounding queries appearing for your site that you didn't expect, you've discovered how AI categorizes your content. If you see queries you should appear for but don't, you've found a content gap. Either way, this data is actionable.

Query Fan-Out: How Copilot Expands a Single Question

A single user question doesn't trigger a single search. Copilot uses query fan-out, expanding one question into multiple sub-queries that explore variations, adjacent intents, and comparisons. Our study of 102K queries documented this pattern across AI platforms.

Here's how grounding and fan-out work together:

Fan-out generates 8 types of query variants from a single user prompt:

1. Equivalent: Same meaning, different phrasing
2. Follow-up: Next logical question
3. Generalization: Broader version of the query
4. Specification: More narrow, detailed version
5. Canonicalization: Standardized form of the query
6. Translation: Same intent in different terminology
7. Entailment: Implied sub-questions
8. Clarification: Disambiguation queries

Why this matters for GEO: Your content needs to be retrievable across multiple reformulations, not just the exact user prompt. A single article that only answers one narrow question will get cited less than content that covers the topic breadth AI systems explore during fan-out.

Think of it this way: when a user asks "What's the best CRM for startups?", Copilot doesn't just search for that.

It fans out into pricing comparisons, feature lists, integration options, startup-specific reviews, and competitor analyses.

If your content covers multiple angles, you'll appear in more of those sub-queries.

5-Step Workflow: From BWT Data to AI Optimization

Step 1: Audit Your Citation Landscape

Start by reviewing the AI Performance dashboard overview:

• Check your total citations count and trend direction
• Identify your top-cited URLs and the topics they cover
• Note whether your average cited pages count is growing or stagnant
• Look for concentration risk: are 3-5 pages driving all your citations?

If you see extreme concentration (a few pages dominating), that's both a strength and a vulnerability. Those pages are clearly authoritative in AI systems, but you need to diversify.

You can do that on our GEO Tool Qwairy.

Step 2: Analyze Grounding Queries

Dive into the grounding queries section:

• Review the sample queries BWT provides
• Classify each by intent type: informational, comparison, how-to, recommendation, problem-solving
• Identify topics where AI retrieves your content vs. topics where it doesn't
• Look for surprising associations, such as queries you didn't expect to appear for

Pay special attention to comparison and evaluation queries. These are the highest-value AI prompts because they trigger multi-source responses where citation matters most.

Step 3: Identify Content Gaps from Fan-Out Patterns

Cross-reference your grounding queries with what you know about fan-out:

• Look for grounding queries that cite competitors but not you
• Map the fan-out variations your existing content doesn't cover
• Prioritize MOFU/consideration queries, which represent 47% of AI grounding activity
• Check if your content covers the breadth of angles fan-out explores (comparisons, alternatives, use cases, pricing)

Step 4: Optimize Content for Grounding

Structure your content so AI systems can easily retrieve and cite it:

• Use clear headings that match grounding query patterns
• Build atomic facts, meaning independently retrievable passages that answer specific questions
• Include specific data points, statistics, and evidence-based claims
• Add schema markup for structured data recognition
• Reference third-party validation and authoritative sources

For detailed guidance on content structure, see the E-E-A-T guide and citation optimization guide.

Step 5: Monitor Across All AI Platforms

BWT only shows Copilot data. To understand your full AI visibility, you need cross-platform monitoring:

• Track the same queries on ChatGPT, Perplexity, Claude, and Gemini
• Compare which platforms cite you and which don't
• Identify platform-specific patterns (some platforms favor different content types)
• Use insights from one platform to optimize for others

See our brand tracking guide for a complete cross-platform monitoring setup.

How Qwairy Completes the Picture

BWT shows Copilot data. GSC shows Google data. But the AI landscape spans far more than two platforms, and managing them separately creates blind spots.

Qwairy connects to both Bing Webmaster Tools and Google Search Console, bringing all your search and AI data into a single dashboard. On top of that, it monitors your brand visibility across all major AI platforms: ChatGPT, Perplexity, Claude, Gemini, and AI Overview.

Here's how the tools work together inside Qwairy:

• BWT data (connected to Qwairy) reveals your grounding queries and citation patterns in Copilot
• GSC data (connected to Qwairy) surfaces volume-validated queries with AI potential
• Cross-platform monitoring tracks those queries across ChatGPT, Perplexity, Claude, Gemini, and tracks your competitor presence

Cross-reference your BWT grounding queries with GSC volume data and Qwairy's AI monitoring results, all from one place. See which topics cite you in Copilot but not in ChatGPT, or vice versa. Where gaps exist, you have clear optimization targets.

What's Next

To start using BWT AI Performance data for optimization:

1. Verify your site in Bing Webmaster Tools if you haven't already (free, takes minutes)
2. Enable AI Performance and review your initial citation data
3. Audit your grounding queries to classify by intent and identify gaps
4. Connect BWT and GSC to Qwairy for automated prompt discovery across platforms
5. Set up cross-platform monitoring to track the same queries on ChatGPT, Perplexity, Claude, and Gemini

Your content is already being used by AI systems. BWT now lets you see how. The question is whether you'll use that visibility to optimize, or leave it to competitors who will.

Start monitoring your AI visibility with Qwairy and connect BWT, GSC, track across all platforms, and turn AI citations into growth.