Microsoft Clarity Now Shows Grounding Queries Behind AI Citations: the Practical Angle

For a long time, the way AI engines decide which pieces of content to cite has felt like a black box. We see the result—a citation or a link in a generated response—but we rarely see the "why" or the "how" behind the retrieval process. When Microsoft Clarity opened up AI citations to all users, it shifted the dynamic. We can now see the specific "grounding queries" that an AI engine uses to locate and pull our content into its answers.

The immediate reaction for many SEOs is a skeptical one: since this is a Microsoft tool, does the data actually matter if your primary audience doesn't use Bing? It is a fair question. However, the value here isn't just in the traffic numbers, but in the visibility into the AI's internal logic. Understanding how a machine decomposes a complex human prompt into a searchable query is a massive advantage for anyone trying to maintain visibility in an AI-driven search landscape.

The Mechanics of AI Grounding Queries

To understand the value of this update, we first have to understand what a grounding query actually is. When a user asks Copilot a question, the AI doesn't simply rely on its pre-trained knowledge. Instead, it translates the user's natural language prompt into a set of simplified search terms. These are the grounding queries. They act as the bridge between a conversational request and the factual data residing on the web.

By accessing this data in Clarity, you can stop guessing what topics the AI associates with your brand. You can identify specific gaps where your content fails to align with the terms the AI is searching for. Furthermore, it allows you to identify pages that the AI is clearly reading—meaning it found the page via a grounding query—but is choosing not to link to or cite. This is a clear signal that the page may be too complex or poorly structured for the AI to extract a concise answer.

Expert Interpretation: The critical takeaway here is the distinction between user intent (the prompt) and machine intent (the grounding query). The tradeoff is that while you gain insight into the machine's logic, you are seeing a filtered version of the user's original question. The decision you should make is to audit your "read but not cited" pages. If the AI is landing on your page but not citing it, the problem isn't your SEO or your ranking; it is your content's "extractability."

Comparing the Retrieval Logic of Copilot and Gemini

While we are looking at Microsoft's data, it is helpful to look at the broader landscape. Both Microsoft Copilot and Google Gemini utilize a framework known as Retrieval-Augmented Generation (RAG). Rather than generating a response based solely on the static parameters they were trained on, these systems dynamically query external search indexes to find real-time information. They then use this retrieved data as a "grounding" context to ensure the final response is accurate and current.

This means that the process of "grounding" is not a quirk of Copilot, but a fundamental architectural choice for most modern LLMs. They are all essentially performing a high-speed search, reading the top results, and synthesizing an answer on the fly.

Expert Interpretation: This matters because it confirms that AI visibility is still heavily tied to retrieval. The tradeoff is that the "traditional" SEO goal of ranking #1 for a keyword is evolving into a goal of being the most "retrievable" source of truth. The decision here is to shift focus from keyword density to "contextual clarity"—ensuring your facts are presented in a way that a RAG system can easily parse and synthesize.

The Relationship Between Bing Rankings and AI Citations

There is a common misconception that AI citations are a separate entity from traditional search rankings. However, the data suggests a tight correlation. Consider the case of a website with a long history and over 1,000 articles that attracts a global audience (including users of Baidu and SwissCows) but receives almost no traffic from Google. Despite the lack of Google traffic, this site saw over 36,000 citations in Copilot.

When analyzing the 147 grounding queries associated with those citations, a clear pattern emerged: the site ranked in the top 20 on Bing for almost every single one of those queries, while it didn't rank for a single one on Google. This proves that Copilot's ability to cite a site is heavily dependent on that site's visibility within the Bing index.

Expert Interpretation: This highlights a significant blind spot in the industry. Because so many of us ignore Bing, we ignore the primary data source for one of the most popular AI assistants. The tradeoff is that you might be missing out on massive AI visibility simply because you aren't monitoring your Bing performance. The decision you should make is to stop treating Bing as a secondary search engine and start treating it as a primary AI discovery engine.

Is Microsoft-Centric Data Useful for Other AI Platforms?

Since the data in Microsoft Clarity comes from Microsoft's own ecosystem, it does not provide a direct window into how Perplexity, Google Gemini, or OpenAI's ChatGPT (which uses its own search mechanisms) are citing your links. These platforms do not share their internal grounding logs with Microsoft.

However, while the data source is skewed toward Microsoft, the insights are platform-agnostic. The way an AI distills a human prompt into a search query is a logical process that remains broadly consistent across different LLMs. If you can see how Copilot decomposes a query, you are seeing a blueprint for how most RAG-based systems operate.

The Assumption of Universal Retrieval Patterns

The working theory is that if a page has a high "Share of Authority" for a complex query in the Bing ecosystem, it is likely because that page is structured perfectly for AI consumption. This usually means the use of clear tables, bulleted lists, and direct, unambiguous answers. If a page is "AI-friendly" for Copilot, it is highly probable that it will be equally appealing to Google Gemini.

That said, this isn't a universal law. Some research suggests that LLMs differ based on positional biases, and some may utilize the SDSR (Search, Distill, Synthesize, Respond) method rather than standard RAG. Additionally, we've seen shifts in the industry, such as ChatGPT beginning to use Google Search as a fallback, whereas it previously relied more heavily on Bing.

Expert Interpretation: The risk here is over-generalizing. The tradeoff is between the efficiency of using one tool (Clarity) as a proxy and the accuracy of knowing exactly how Gemini or ChatGPT behaves. The decision you should make is to use Clarity as a "structural guide." If Copilot loves a specific format on your site, replicate that format across your other high-value pages to increase your chances of being cited by other LLMs.

Turning Grounding Data into a Content Strategy

Ultimately, the value of the grounding queries dashboard isn't in the 1-to-1 reflection of your total AI traffic, but in the structural patterns it reveals. When you see a page earning citations in Copilot, it is a signal that the page is doing something right: the topic is well-scoped, the answers are clear, and the content aligns with how AI engines translate human curiosity into search terms.

Equally important is the "gap data." If you find pages that rank highly in Bing but never appear as grounding queries, you have found a mismatch. This suggests that while the page is "search-friendly" for a human user, it is not "retrieval-friendly" for an AI. It may be too wordy, the main point may be buried, or the structure may be too idiosyncratic for a machine to easily distill.

Expert Interpretation: This transforms your content audit from a subjective exercise into a data-driven one. Instead of asking "Is this a good article?", you can ask "Is this article retrievable?" The decision is to prioritize the rewriting of high-ranking but low-citation pages. By simplifying the layout and aligning the phrasing with the grounding queries you've discovered, you can bridge the gap between being "indexed" and being "cited."

Practical next steps

The useful part is not only the idea itself, but the operating habit behind it. Use it as a checklist for decisions: what deserves attention now, what should be monitored, what needs a stronger evidence base, and what can wait until the system has more scale.