Microsoft Clarity Reveals AI Citation Source Queries

When Microsoft Clarity rolled out AI citation visibility to all users, it handed SEO professionals a new testing ground for understanding how artificial intelligence surfaces their content. For the first time, we can see the exact grounding queries an AI engine uses to pull information from our websites. But because this data comes from a Microsoft tool, a pressing question emerges: Is any of this useful if your audience never touches the Bing ecosystem?

How Microsoft Clarity Grounding Queries Work

When a user asks Copilot a question, the system translates that natural language into simpler search terms known as grounding queries. These are used to find factual information on the web before the AI constructs its answer. For website owners, this data becomes a diagnostic tool. You can identify gaps where your content doesn’t align with what the AI is actually searching for, simplify pages that the AI reads but doesn’t link to, and even apply those clean, structured layouts to improve your Google search results.

Copilot Versus Gemini: A Structural Comparison

Both Microsoft Copilot and Google Gemini rely on retrieval-augmented generation (RAG) frameworks. Instead of generating answers solely from pre-trained parameters, they dynamically query external search indexes to pull real-time data, using that context to ground their final responses.

| Feature | Microsoft Copilot | Google Gemini | |—|—|—| | Structure | Uses a query translator, Bing index search, and OpenAI models to write the final text. | Uses a query translator, Google Search, and Google’s Gemini models to write the final text. | | Pulling Sources | Relies on the Bing index and Microsoft Graph to scan web pages, emails, and Microsoft 365 files (with permissions). | Relies on Google Search and Google Workspace to scan web pages, Google Drive files, and Gmail (with permissions). | | Synthesising Answers | Focuses on direct answers using structured lists, tables, and bullet points. | Focuses on creative, conversational answers built to handle text, images, and code simultaneously. |

Does Ranking in Bing Matter? Yes, but With a Caveat

One website I manage performed exceptionally well in Copilot, earning over 36,000 citations across all queries. Clarity doesn’t reveal the original user prompts, but it does show the grounding queries and key phrases that triggered the AI to retrieve the site’s content. This site has been running for years, merged an older domain in 2019, and hosts more than 1,000 articles. Google barely sends it traffic, and third-party SEO tools often flag it as spam due to non-English backlinks from search engines like Baidu, CocCoc, and SwissCows. Given that, the 36,000 citations were a surprise.

To understand the Copilot love, I tracked 147 grounding queries against their rankings in both Google and Bing. Bing ranked all but six of those queries, with most landing in traffic-driving positions within the top 20. Google didn’t rank a single one.

Is Clarity’s Data Useful Outside the Microsoft Ecosystem?

Because this is a Microsoft tool, the backend data feeding the dashboard primarily captures how your site is cited across Microsoft’s AI surfaces, including Copilot and Bing generative search. It does not provide a direct window into how OpenAI’s ChatGPT (using its own search), Google Gemini, or Perplexity are citing your links. Those platforms do not share their internal grounding logs with Microsoft. And historically, the SEO industry has neglected Bing.

Even so, the data collection source being skewed toward Microsoft’s AI engine does not make the insights useless. The patterns revealed are highly transferable to broader, platform-agnostic AI optimization strategies.

Can We Assume Other LLMs Retrieve Data the Same Way?

AI engines, whether Google Gemini or Microsoft Copilot, use similar RAG frameworks to fetch data. If the Bing ecosystem flags that a specific page has a high Share of Authority for a complex query, it means that page is structured perfectly for AI consumption with clear tables, bullet points, and direct answers. Research suggests you can replicate that formatting across your site to appeal to Google Gemini as well.

However, this assumption can be challenged. Other research indicates that the similarity between LLMs depends on positional biases, and some models may use the SDSR method rather than RAG. SEO researchers have also found that ChatGPT has started using Google Search as a fallback, when it was initially powered by Bing.

In Summary: Treat Clarity as a Lab Environment

If your audience doesn’t touch the Microsoft ecosystem, this dashboard won’t give you a perfect one-to-one reflection of your total AI traffic. But that doesn’t make the data useless. What grounding queries reveal is how AI systems distill user intent into retrievable search terms. That process is broadly consistent across platforms, even when the underlying indexes differ. A page earning citations in Copilot is doing something right structurally with clear answers, well-scoped topics, and content aligned with how AI engines translate questions into queries. The Bing dependency tells you where the data comes from. The structural patterns tell you something more transferable.

The gap data is equally instructive. Pages your site ranks for in Bing that never appear as grounding queries signal a mismatch. Either the content isn’t structured for AI retrieval, or the topic isn’t one AI engines are actively grounding answers around. Treat Clarity’s Citations dashboard as a useful proxy and window into how LLMs read, slice, and credit your website’s content. Even if Copilot isn’t your primary AI traffic source, the patterns it surfaces are worth paying attention to.