Definition
Prompt research is the process of finding, organizing and prioritizing the questions people ask AI assistants such as ChatGPT, Perplexity and Google AI Mode about your category. It is the GEO equivalent of keyword research: the output is a list of prompts worth tracking and worth creating content for.
It differs from keyword research in three ways. Prompts are full questions with context ('which CRM works for a 10-person agency that already uses Google Workspace?') rather than two- or three-word phrases. One prompt can trigger many searches behind the scenes through query fan-out, so the prompt and the searches it produces both matter. And AI platforms do not publish the questions users ask. Google's generative AI performance report in Search Console, rolled out to all sites in August 2026, shows impressions in AI Overviews and AI Mode by page, country and device, but no query-level data. Prompt research therefore relies on proxies and real customer language.
The most reliable sources are:
- Customer conversations: support tickets, chat logs, sales call notes and onboarding questions, in the words customers actually use.
- Public discussions: Reddit threads, community forums and Q&A sites where people ask for recommendations in your category.
- Reviews: G2, Trustpilot or app store reviews, especially the comparisons ('better than', 'alternative to') people make.
- Search data: Google Search Console queries, 'People also ask' questions and the keywords your pages rank for, rewritten as natural-language questions.
- AI answers themselves: once you track a first set, the prompts where competitors appear and you do not are the strongest leads.
Once collected, prompts are organized so the results can be read. Group them by topic, by buyer stage or intent (informational, commercial, transactional), and by type: organic category questions, questions that name your brand, and head-to-head comparisons. A list made only of branded prompts will flatter you, because AI answers usually mention the brand a question names. The organic and comparison prompts show whether you are discovered by people who do not know you yet. Where the same question is asked by different audiences, persona-based prompt tracking adds the context of who is asking.
Prioritization uses estimated demand and difficulty. Prompt volume estimates how often a question is asked, usually from the search demand behind its keywords, and a difficulty score estimates how hard it would be to become visible. Commercial and transactional prompts close to a purchase deserve weight even with low volume. Promptwatch's Prompt Explorer, for example, generates suggestions from Search Console, ranking keywords, People also ask, a seed topic or one of your own pages, and attaches volume and difficulty to each.
The result feeds two things: the prompt set you monitor (see prompt monitoring) and your content plan. Our guide on finding the right queries to track walks through the full process. Treat the list as living: add prompts as products, competitors and customer language change, but avoid editing the wording of prompts already being tracked, since that breaks the trend line. Related ideas include latent intent and natural-language queries.
Examples of Prompt Research
- A payroll software company exports 300 support tickets, clusters them into 40 recurring questions and rewrites them as prompts such as 'how do I run payroll for contractors in two US states?'.
- An outdoor gear brand reads Reddit threads in r/CampingGear and r/Ultralight and adds prompts built from the comparisons people make, like 'is a 20-degree quilt warm enough for the Sierra in October?'.
- A B2B analytics vendor turns its top 100 Search Console queries into natural-language prompts and finds that 30 of them return AI answers naming three competitors but not the vendor.
- A bank's marketing team starts with 80% branded prompts, realizes its visibility numbers look inflated, and rebalances the set toward organic questions about mortgages and savings accounts.
