How Real Users Actually Prompt AI: Implications for GEO Strategy

For months, marketers have fixated on crafting hyper-structured, long-form prompts to game AI search—assuming users follow the same playbook. But two recent surveys from Stella Rising reveal a far more nuanced reality: real AI prompts blend the brevity of traditional search queries with deeply personalized context. This dual identity demands a rethinking of GEO strategies, balancing familiar SEO tactics with new approaches tailored to AI’s unique user interactions.

The Two Faces of Real AI Prompts

Stella Rising’s 2026 January survey of 524 active LLM users (30-day users of ChatGPT, Copilot, or Gemini, with a ±4.3% margin of error at 95% confidence) paints a clear picture: most prompts aren’t the verbose, curated requests seen in influencer content.

1. Two-thirds of respondents use prompts of 15 words or fewer, with only 12% fitting the "AI influencer" definition of a "true prompt."
2. About 60% of prompts take the form of questions, while just 9% are direct commands. In shoe-related tasks, the median prompt length is a mere 8 words.
3. Semrush data confirms this overlap with traditional search: ChatGPT’s search mode sees prompt lengths ranging from 4.2 to 8.7 words, mirroring Google query patterns. Long, structured prompts are largely limited to non-search tasks like writing or coding.
4. Otterly.AI analysis adds context: real user prompts are 71% longer than synthetic prompts crafted by marketers, but their median length is still just 12 words—hardly the epic prompts many brands prepare for.

The Rise of Context-Rich Prompts

While short, keyword-like prompts still exist, their dominance is fading. Stella Rising’s 2025 August survey of beauty consumers found 50% of prompts were SEO-style keywords (short, vague, brand or attribute-focused). By January 2026, that share dropped to 30%, with 70% of prompts now longer and infused with personal context.

1. 24.5% of prompts include the word "best," reflecting a focus on recommendations.
2. 28% mention price or budget constraints, a detail rarely included in standard search queries.
3. 16% explicitly reference location, aligning with Local Falcon 2025 data showing AI overviews appear in 92% of local information queries, compared to just 15% of simple local pack searches.
4. 32% include personal attributes like size, profession, or health status, adding layers of specificity that traditional search can’t capture.

The User Embedding Layer: Hidden Value for Brands

This shift toward context-rich prompts creates what’s known as the "user embedding layer"—a personalized profile LLMs build through repeated interactions. Nearly one-third of users share personal information (like shoe size, nursing profession, or gluten intolerance) in AI prompts that they’d never include in a Google search.

As users grow trust in their LLM of choice, they’re more likely to submit context-heavy requests that drive purchase decisions. These prompts never appear on search engine results pages (SERPs) or in keyword tools, making them invisible to traditional SEO tracking. However, brands that appear in recommendations tied to these prompts benefit from far higher relevance, as the AI has already aligned the user’s needs with the brand’s offerings.

Synthetic Prompts: Value and Limitations

Synthetic user personas can help brands simulate the user embedding layer, testing how their content performs in different context-rich scenarios. But they have clear limitations: they can’t replicate the messiness of real prompts, including typos, fragmented dialogue history, or long-term user memory.

To maximize value, synthetic prompts should be paired with real-world data sources, such as:

1. Customer interviews and feedback
2. Social media search queries
3. Support ticket inquiries
4. Question-based queries from Google Search Console

Layered Tracking for AI Visibility

Brands need a three-pronged approach to track their AI search visibility effectively:

1. Short, keyword-like prompts: Account for 30% of user queries, with over 90% triggering real-time web searches. Focus on filtering out generic head terms or single-brand keywords to prioritize high-intent queries.
2. Synthetic persona prompts: Use these to benchmark how your brand and competitors appear in context-rich scenarios, such as budget-limited or location-specific requests.
3. Qualitative context prompt library: Build a repository of real, complex prompts to validate whether your content matches the nuanced needs of actual users.

AI Search: Shifting User Behavior and Traffic Trends

User trust and adoption of AI search are skyrocketing, with profound implications for brand traffic:

1. 68% of users trust ChatGPT recommendations more than Google, citing detailed, ad-free, personalized results.
2. A NIM study found ChatGPT helps consumers make purchasing decisions more efficiently and accurately than Google.
3. Heavy AI users are less trusting of Google: 2026 data shows the more someone uses AI, the lower their trust in traditional search.
4. Yext’s 2026 report notes 35% of heavy AI users view ChatGPT as their primary source of high-quality information.
5. Half of active AI users now use AI daily or multiple times for tasks they previously did on Google, and 37% start their search journey with AI instead of traditional engines.
6. OpenAI’s February 2026 data reveals ChatGPT has 9 billion weekly active users—double the previous year’s figure.
7. 85% of users click on source links recommended by AI, with 21.9% always clicking through.
8. Conductor reports AI referral traffic grew 357% year-over-year in 2026, now accounting for 1.08% of total website traffic. Semrush found ChatGPT external referrals rose 206% in 2025.
9. Emarketed data shows AI referral visitors have a 4.4x higher conversion rate than regular organic traffic.
10. 34% of users engage in voice chat with AI daily or more frequently.
11. Ahrefs data indicates AI overviews reduce click-through rates for top-ranking pages by 58%.

Actionable GEO/SEO Optimizations for AI Search

To adapt to these trends, GEO and SEO teams should prioritize three key actions:

1. Audit prompt tracking setup: Ensure your tools capture both synthetic and real prompt data to get a complete view of AI visibility.
2. Map context-rich personas to core categories: For your key product lines, create user personas that reflect the context they’d include in AI prompts (e.g., budget-conscious parents, plus-size shoppers) and audit your content to ensure it addresses these specific needs.
3. Continue optimizing for keyword-like prompts: Since 30% of prompts still follow traditional SEO patterns—and over 90% of these trigger web searches—maintain your core SEO efforts to capture this high-intent traffic.

The era of one-size-fits-all AI search strategies is over. Real user prompts blend the familiarity of short keywords with the depth of personalized context, requiring brands to balance traditional SEO tactics with new approaches tailored to AI’s unique capabilities. By embracing layered tracking, leveraging both synthetic and real prompt data, and optimizing for both prompt types, brands can position themselves to capture the growing share of traffic and trust shifting to AI search.