Hard To Know What Prompts Customers Ask Ai Tools

• Keyword tools measure queries that pass through Google's search box, and that data does not reach AI assistants.
• Not all sources carry the same weight.
• AI prompts are longer, more conversational, and more contextual than keywords.
• Start by collecting far more candidates than you need, then cut hard.
• Treat vendor volume figures as directional at best. For instance, according to Cognizo's 2026 analysis citing a Gartner survey, 45% of B2B buyers used generative AI during a recent purchase, drawing on an average of seven information sources before deciding.

Keyword tools measure queries that pass through Google's search box, and that data does not reach AI assistants. According to Otterly.ai's prompt research guide, there is no Google Search Console equivalent for ChatGPT. OpenAI does not disclose search data, so marketers cannot see which prompts people type, how often they appear, or which brands get named.

Prompt research therefore has to start from the buyer rather than from a volume report. The gap matters because buyers now split their research. According to G2, as cited by Rankbits, half of B2B software buyers start on ChatGPT more often than on Google. The questions you never see are the ones shaping shortlists. According to Rankbits' 2026 prompt research guide, people type over 2.5 billion prompts into ChatGPT every day.

Not all sources carry the same weight. Direct buyer speech ranks highest: sales call transcripts, support tickets, and 'where did you hear about us' form answers show the exact words prospects use. Otterly.ai's guide also lists survey responses and Google Search Console queries with conversational phrasing as usable sources. Community threads and review sites come next, where people describe problems in their own language. Autocomplete and People Also Ask boxes sit lower, because they reflect search phrasing that has been compressed into keywords. As Linkeddit notes, rewriting keyword lists as questions keeps that compression and does not reproduce real buyer language, so treat keyword-derived prompts as hypotheses to test rather than evidence.

Ranking reflects how closely each source reflects typed buyer language, not a measured conversion rate. According to G2, as cited by Rankbits, half of B2B software buyers now start their research on ChatGPT more often than on Google.

AI prompts are longer, more conversational, and more contextual than keywords. According to Semrush's analysis, as cited by Zene, the average ChatGPT prompt was 23 words, compared with 4.2 words for ChatGPT Search queries. Length also rises with buying intent. Position Digital's analysis found prompts grew from 11.5 words for informational queries to 18.2 words for branded ones.

Structure follows a pattern: 79% of SaaS buyer prompts are questions, and 48% of commercial prompts ask for 'best' or 'top' options. Google adds a further complication. According to Google's developer documentation, as cited by Zene, AI Overviews and AI Mode may run query fan-out, issuing several related searches across subtopics to assemble one answer. A single buyer prompt can therefore trigger many hidden sub-queries.

Start by collecting far more candidates than you need, then cut hard. Deduplicate near-identical wordings and remove prompts that name no category or problem. Tag each remaining prompt by funnel stage: problem-aware, category exploration, comparison, or brand.

Weight comparison and brand prompts above general problem prompts when budget is limited. These sit closest to a purchase decision.

Keep the set small enough to review by hand.

• Deduplicate near-identical wordings
• Remove prompts that name no category or problem
• Tag each prompt by funnel stage
• Record responses across repeated runs

A single prompt's answer varies between runs, so read results at the topic level rather than trusting any one output. Linkeddit makes the same point about phrasing: judge a cluster of prompts together, not a lone wording.

Treat vendor volume figures as directional at best. Kitbase reports there is no public dataset of prompt volumes for AI engines comparable to search volume, so any precise number a tool shows is an estimate built on proxies. A more defensible approach ranks prompts by evidence of real buyer intent:

• Whether the wording came from a sales call
• A support ticket
• A form response

Ask any vendor how its volumes are produced and whether it observes actual prompts or models them. Where the method is not documented, say so in your planning notes and rely on the intent evidence instead. Prompt volume should inform prioritisation, never a forecast of traffic.

Teams feel this gap most when AI answers recommend competitors and nobody can say which question caused it. Category leaders need the comparison and 'best for' prompts that decide shortlists, because Position Digital's analysis found 48% of commercial SaaS prompts ask for recommendations. Agencies managing several clients need one repeatable method applied per account.

Once a tracking set exists, Fastlook's Citation Analytics tracks where your brand appears across ChatGPT, Perplexity, Gemini, and Google AI Overviews, and its Page Engine can publish citation-ready pages for the gaps you find. Start with the intent sources you already own, and build the set this week rather than waiting for a perfect volume number.

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• Check If AI Tools Mention Your Brand: A Repeatable Protocol