Track AI Search Prompts in Google Search Console

The challenge of identifying which AI prompts to track for visibility is a pressing concern for many businesses navigating this new landscape. Unlike traditional search with its established keyword tools, the world of large language models (LLMs) operates as a data black box, with platforms like OpenAI and Google unlikely to fully disclose user query data. This leaves marketers searching for creative methods to understand how potential customers are conversing with AI about their brands and services.

Interestingly, evidence suggests that some AI-generated queries are finding their way into Google Search Console. Last fall, reports confirmed that searches originating from ChatGPT had leaked into certain Search Console profiles, a privacy issue that was reportedly addressed. More recently, it has been confirmed that data from Google’s own AI Mode will be available within the platform. This indicates that Search Console is evolving to capture how users search within LLM interfaces, providing a potential, albeit indirect, data source.

To mine this data, one effective technique involves filtering for exceptionally long search queries, which often resemble conversational prompts. Within Search Console’s Performance report, you can apply a custom regex filter, specifically `^(?:\S+\s+){9,}\S+$`, to isolate queries that are ten words or longer. The results are frequently eye-opening, revealing search strings that read less like traditional keywords and more like detailed instructions or questions posed to an assistant.

Examples of such query patterns include requests to “map out a full day” at a national park with specific activities, or inquiries asking for the “best platforms” to solve a complex business problem with listed criteria. While there’s no definitive proof these are from ChatGPT or AI Mode, their conversational nature provides a valuable window into customer intent expressed through longer, more nuanced query strings. As one industry expert notes, “we’re doing business, not science,” and this available data can inform strategy in a landscape moving toward zero-click interactions.

For analyzing the exported list of potential prompts, Claude has proven to be a highly effective tool. It can process the data to perform behavioral analysis, identify themes, and uncover trends. By asking it specific questions, such as what customers are asking about your brand, how they frame their questions, or what product characteristics they care about, you can extract actionable business insights. This analysis might reveal lingering PR issues, unexpected geographic demand, or that your product is consistently used as a benchmark against competitors.

Taking it a step further, you can task Claude with generating specific prompt tracking recommendations based on the patterns it discovers in the data. This moves your strategy beyond guesswork, grounding it in actual user behavior observed through Search Console. While a perfect system doesn’t exist, user prompts for the same goal can vary immensely, this method creates a far more informed starting point. At the very least, it uncovers unique query opportunities and helps identify scalable, common themes to apply to your tracking efforts, moving you closer to understanding the new language of AI search.

(Source: Search Engine Land)