How real people actually prompt AI — and what it means for GEO

Most individuals aren’t utilizing AI the way in which GEO discussions typically assume. Two surveys of AI customers carried out by Stella Rising discovered that many prompts nonetheless look remarkably much like conventional search queries. (Disclosure: I’m the VP of Search engine optimisation at Stella Rising.)

One survey centered on a beauty-oriented shopper panel in August 2025, whereas the opposite surveyed a broader general-audience inhabitants in January 2026. Throughout each research, prompts have been quick, typically keyword-driven, and far nearer to a Google search than the frilly immediate templates fashionable in AI advertising and marketing circles.

On the identical time, a rising share of customers are including private context, corresponding to their finances, location, career, age, well being considerations, or preferences. These particulars give AI techniques much more info than a conventional search question ever may, creating a brand new layer of personalization that influences suggestions and model visibility.

The mixed findings counsel that GEO methods have to account for each realities: Many AI searches nonetheless resemble basic key phrase queries, whereas the highest-value suggestions more and more emerge from prompts wealthy with private context. That’s the place the chance — and the measurement problem — lies.

Lots of people are nonetheless typing prefer it’s 2008

The largest takeaway throughout each surveys is that the median AI consumer remains to be throwing a key phrase over the wall and hoping for the perfect.

Within the general-audience research from January:

• Two-thirds of respondents reported writing prompts of 15 phrases or fewer. 
• Solely 12% wrote one thing that may qualify as a “actual” immediate by the requirements of an AI influencer thread. 
• About 60% phrased their queries as questions, whereas solely 9% gave a direct command. 

That mirrors what Pew Analysis has been seeing extra broadly — 34% of all U.S. adults now use ChatGPT, roughly double the 2023 share, and 58% of adults underneath 30 use it.

After we ran a state of affairs job asking respondents to put in writing the immediate they’d ship in the event that they wanted a brand new pair of sneakers, the median reply was eight phrases. Actual examples from the panel included:

• “Sneakers close by”
• “Tennis sneakers”
• “Nike”
• “Women tennis sneakers measurement 7 close to me”
• “Finest worth for mountain climbing sneakers”

This traces up with Semrush’s clickstream data on ChatGPT’s search mode, which reveals the typical immediate size is 4.2 to eight.7 phrases, primarily the identical as a Google question.

Longer, structured prompts have a tendency to look solely when customers are doing one thing aside from search, corresponding to drafting, coding, or inventive work.

For AEO and GEO work, that’s the half to internalize. In the event you’re optimizing for prompts like “Evaluate the highest 5 orthopedic-approved strolling sneakers underneath $150 for plantar fasciitis with 4.5+ star rankings,” you’re optimizing for the improper distribution. 

Actual prompts run 71% longer than the artificial ones entrepreneurs are likely to invent, however the median remains to be solely 12 phrases, Otterly.AI’s evaluation discovered.

The shift between the 2 surveys

Within the August 2025 survey, we labeled roughly 50% of the free-text prompts as “Search engine optimisation-keyword-shaped,” which means quick, ambiguous, and brand-and-attribute-driven. By the point the January 2026 survey got here again, that share had dropped nearer to 30%. The remaining 70% had grown longer and extra contextualized.

Just a few findings are value carrying ahead:

• 24.5% of all prompts embody the phrase “greatest.” In the event you’re not showing in “greatest Generative Engine Optimization (GEO)” responses, you’re lacking one of many highest-intent slots.
• 28% of prompts point out worth or finances constraints. Customers aren’t simply buying. They’re buying with a quantity of their head.
• 16% of prompts are explicitly location-based. The “close to me” question sample has efficiently migrated from Google to LLMs.
• 32% of prompts embody private attributes (e.g., measurement, career, well being situation, life stage, and so forth.). That is an important quantity on the web page, and we’ll come again to it.

On location particularly, the 16% determine traces up with what Native Falcon’s 2025 analysis confirmed for AI search total: AI Overviews now seem on 92% of informational local queries, however solely on 15% of straightforward local-pack queries. The intent is shifting into LLMs sooner than the availability of optimized native content material for AI engines.

One caveat: These have been two totally different surveys with two totally different audiences. The January 2026 general-audience pattern was structurally extra transactional than the August 2025 beauty-focused panel, which partially explains why fewer prompts regarded like keyword-style searches and extra regarded like full requests. I wouldn’t over-index on the “prompts are evolving” narrative, however I’d completely take the directional learn.

The consumer embedding layer is the place this will get attention-grabbing

The 32% determine (prompts containing actual private context) is essentially the most under-discussed discovering within the dataset.

Practically one-third of customers are willingly handing LLMs info that no Google question would usually carry, corresponding to their measurement, job, coaching plan, residing scenario, or children’ ages. We see prompts within the information like:

• “What sneakers would you suggest for every day standing at work?”
• “Discover me an economical pair of trainers that I can order on Amazon. My measurement is males’s 10.”
• “Please inform me the highest 5 sneakers for broad toes in a measurement eight for girls which can be snug, fashionable, underneath $120, and that youthful folks received’t make enjoyable of for a Gen X particular person like me.”

That final one alone packs in gender, foot width, measurement, finances, model intent, generational identification, and an actual social anxiousness. No conventional search question was ever going to floor all of that.

That is the consumer embedding layer at work. When somebody interacts with ChatGPT or Gemini repeatedly, the mannequin builds a profile of who they’re, which more and more persists by way of reminiscence. The consumer is, in impact, coaching the assistant on themselves. As soon as that belief is established, they cease writing floor queries and begin writing requests that assume the assistant is aware of them.

That shift has two implications for a way manufacturers ought to take into consideration visibility:

• The prompts that drive buy choices are sometimes not those that present up in a SERP or key phrase instrument. An actual Gen X lady asking about wide-fit, $120, “received’t-get-made-fun-of” sneakers won’t ever seem as a tracked SERP key phrase. However that’s the immediate that decides whether or not your shoe results in the advice set.
• The worth of a model quotation will increase considerably when it seems inside a context-rich immediate. If the mannequin is already factoring in consumer attributes, the manufacturers it surfaces are prefiltered for relevance. That’s a a lot higher-quality impression than a generic blue hyperlink.

The place artificial prompts match — and the place they don’t

A standard tactic in GEO immediate analysis is to assemble artificial personas (“I’m a 38-year-old product supervisor coaching for a half marathon in Boston who prefers manufacturers centered on sustainability…”) after which use these personas to stress-test which manufacturers an LLM surfaces underneath totally different eventualities. There’s actual advantage to the strategy. If the consumer embedding layer is doing the heavy lifting within the reply, the one option to simulate the reply is to simulate the consumer.

However artificial prompts don’t seize every little thing. Actual prompts are messy, layered, and influenced by current dialog historical past, persistent reminiscence, and alerts the mannequin has picked up over weeks of use. You’ll be able to craft a 50-word persona and nonetheless miss the nuance of a consumer who has been speaking to ChatGPT about their day, preferences, and household for six months.

As a substitute, use artificial prompts to map the personas your model must be seen to, however don’t deal with the ensuing visibility scores as floor reality. Mix them with actual immediate information wherever doable. That may imply buyer interviews, social search patterns, help tickets, or regex pulls of question-shaped queries from Google Search Console.

What to really observe

This naturally results in the subsequent query: Do you have to observe Search engine optimisation key phrases in your AI visibility platform if one-third of actual prompts seem like Search engine optimisation key phrases?

The reply is sure, with one filter.

Throughout the final quarter, our crew has seen net retrieval charges on tracked prompts climb sharply. On a number of shopper accounts, greater than 90% of monitored prompts now set off reside net search inside ChatGPT or Google’s AI Mode. 

When that occurs, the LLM is successfully working a real-time SERP and synthesizing the outcome. Meaning the quick, keyword-shaped prompts we recognized — roughly 30% of the overall — are nonetheless very a lot in play. They behave like AI-flavored Google queries and ought to be tracked accordingly.

The filter is that this: Don’t waste monitoring slots on prompts which can be pure head phrases or single-brand queries. These are prone to be answered from mannequin weights or quick canned responses fairly than retrieval, and so they received’t offer you a helpful learn on visibility.

Right here’s a sensible setup:

• An artificial-persona immediate set that workout routines the consumer embedding layer, mapped to the personas your model really must win. Use this to floor which rivals a mannequin defaults to underneath totally different consumer situations.
• An actual-prompt set sourced from question-shaped GSC queries, buyer panel inputs, and regex-extracted “who/what/the place/can/ought to” patterns. These are the quick, retrieval-triggering prompts most customers nonetheless write.
• A small qualitative library of messy, context-rich actual prompts pulled from the kind of work we did for the research. Use it to sanity-check whether or not your content material really solutions the query the consumer is asking, not the query your key phrase instrument says they’re asking.

At that time, you’re not simply monitoring AI visibility. You’re monitoring it throughout the total spectrum of how actual customers get to your content material, from a three-word “good strolling sneakers” question to a 40-word “I’m a 60-year-old with plantar fasciitis…” request.

What the broader information tells us about AI search

A handful of further findings from the January 2026 survey assist clarify why these immediate patterns matter.

Customers more and more belief AI suggestions

As much as 68% of customers belief ChatGPT’s suggestions greater than Google’s, with most citing element, lack of adverts, and personalization as the explanations. 

AI search is turning into a every day behavior

Half of energetic AI customers use these instruments every day or a number of instances per day to finish duties they used to do on Google.

• Search Engine Land reported 37% of consumers now begin searches with an AI instrument as a substitute of Google.
• OpenAI’s February 2026 numbers put ChatGPT’s weekly energetic customers at 900 million — greater than double a yr earlier.

Citations nonetheless drive visitors

85% of customers click on by way of to cited sources no less than a number of the time; 21.9% all the time do. The point out isn’t the tip of the funnel. 

• Conductor’s 2026 benchmarks confirmed AI referral visitors up 357% year-over-year. 
• Semrush reported outbound referrals from ChatGPT up 206% in 2025. 
• Emarketed noticed AI-referred guests changing at 4.4x the speed of normal natural. 
• Quantity remains to be small (Conductor pegs it at round 1.08% of whole visitors), nevertheless it punches effectively above its weight class.

Voice could lastly be having its second

34% of customers at the moment are utilizing voice chat every day or extra typically. That is the primary dataset I’ve seen that truly delivers on the “voice search will matter” promise we’ve been listening to for a decade. 

It’s value pairing all of this with Ahrefs’ newest AI Overviews CTR analysis: The presence of an AI Overview correlates with a 58% decrease clickthrough charge for the top-ranking web page. The visitors that does come by way of is certified. The visitors that doesn’t is gone

AI search is settling right into a richer, extra personalised type. The intent stack is identical one Google has all the time served. What’s new is the embedding layer and the monitoring calls for it entails. That creates a transparent set of priorities for Search engine optimisation and GEO groups.

What adjustments — and what doesn’t

Listed below are three issues you are able to do with this info if you happen to’re an Search engine optimisation lead, content material lead, or technique lead:

• Audit your prompt-tracking setup: If it’s all artificial prompts or all keyword-shaped prompts, you’re lacking half the image. Construct the layered framework outlined above.
• Map your content material to the consumer embedding layer: In your prime classes, record the personas (e.g., age, life stage, career, situation, finances) probably to hold actual prompts into AI search. Then test whether or not your PDPs, weblog content material, and FAQs really reply these folks’s questions.
• Don’t abandon the Search engine optimisation-keyword work: Roughly one-third of actual prompts nonetheless seem like basic search queries. With net retrieval working at 90%+ on most of the prompts we monitor, the hole between an Search engine optimisation key phrase and an AI immediate is narrower than the GEO discourse implies.

The conduct change is actual. The sophistication of AI thought leaders’ prompting is partly right here and partly oversold. Most individuals are nonetheless doing Google-style searches. They’re simply looking inside an interface that is aware of extra about them.

If that’s the place the viewers is, that’s the place now we have to optimize.

Methodology

Each research referenced on this article have been carried out by the Stella Rising crew. You’ll be able to learn it in “New Data: How Consumers Use LLMs for Search in 2026 (And What It Means for GEO).” 

The August 2025 research surveyed 178 members of Stella’s Glimmer Insights neighborhood, 113 of whom have been energetic LLM customers. 

The January 2026 research surveyed 524 energetic LLM customers by way of Centiment, outlined as having used ChatGPT, Copilot, or Gemini within the earlier 30 days, with a margin of error of roughly ±4.3% on the 95% confidence degree. 

Given its smaller measurement and category-specific composition, the August 2025 panel ought to be seen as directional fairly than statistically consultant of the broader U.S. AI consumer inhabitants.