How Real People Actually Prompt AI, and What It Means for GEO

A lot of people are still typing like it's 2008

The biggest takeaway across both surveys is that the median AI user is still throwing a keyword over the wall and hoping for the best. In the general audience study from January: Two thirds of respondents reported writing prompts of 15. The measurement question is whether this signal changes a decision, not whether it adds another number to a dashboard. Useful reporting connects visibility, engagement, and business outcomes without pretending every AI influenced journey will produce a clean click path. This connects with search visibility when the same signal needs a clearer operating decision. A useful companion note is to Improve Your Brand’s LLM Visibility, because it looks at a nearby part of the same system.

The reporting question is whether this signal changes a decision. If it only creates another number in a dashboard, it adds noise. If it helps separate profile activity, website visits, calls, bookings, and direction requests, it can make local performance easier to understand.

The shift between the two surveys

In the August 2025 survey, we classified roughly 50% of the free text prompts as "SEO keyword shaped," meaning short, ambiguous, and brand and attribute driven. By the time the January 2026 survey came back, that share had dropped closer. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The operational question is whether the public business data is complete enough to support the query. Hours, categories, services, reviews, photos, and page content need to reinforce each other so Google can understand the business in a specific situation, not only as a generic listing.

The user embedding layer is where this gets interesting

The 32% figure (prompts containing real personal context) is the most under discussed finding in the dataset. Nearly one third of users are willingly handing LLMs information that no Google query would normally carry, such as their size,. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The risk is usually hidden in the execution layer. A page can look fine to a human and still fail for an automated visitor if the form, call to action, rendering path, or confirmation step is not accessible enough for the agent to complete the task. The same pattern also shows up in AI Recommendation Sets Leave Some Brands Out, where the practical question is how the signal becomes visible.

Where synthetic prompts fit, and where they don't

A common tactic in GEO prompt research is to construct synthetic personas ("I'm a 38 year old product manager training for a half marathon in Boston who prefers brands focused on sustainability…") and then use those personas to. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The useful check is whether this improves the system behind search performance, not only the words on the page. Internal links, crawlable content, clear entities, current evidence, and a sensible page structure all help the recommendation become easier to trust.

A lot of people are still typing like it's 2008

The shift between the two surveys

The user embedding layer is where this gets interesting

Where synthetic prompts fit, and where they don't

What to actually track

This naturally leads to the next question: Should you track SEO keywords in your AI visibility platform if one third of real prompts look like SEO keywords? The answer is yes, with one filter. Across the last quarter, our team has seen web. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.