Prompt research is the practice of collecting the actual, full-sentence questions people type into AI systems like ChatGPT, Gemini, and Perplexity before they buy — then organizing those questions into a structured library you can build content around. You find them by mining sales calls, support tickets, site searches, reviews, and community threads, recording the exact words customers use, and testing those prompts across AI platforms to see who gets cited.
At Grid13 we build automated blog content that is optimized for both Google and AI answer engines, so we spend a lot of time watching which questions AI actually answers — and which brands it names. What follows is the method we use, the tools involved, and the mistakes we watch clients make.
What is prompt research, and why should a marketing lead care?
Keyword research told you what people typed into a search box: two or three words, stripped of context. Prompt research tells you what people ask — full questions with a budget, a use case, a comparison, and a level of experience baked in. That shift matters because the volume is enormous. ChatGPT alone handles roughly 2.5 billion prompts per day globally, with about 330 million coming from users in the United States, according to The Digital Elevator. Those are conversations, not keywords, and most of them never touch a traditional search result page.
The commercial signal is just as clear. Bain's analysis of Sensor Tower data found a 70% overall increase in ChatGPT use alongside a 25% rise in shopping-related prompts. People are asking AI what to buy. If your content is not written around the shape of those questions, you are invisible at the exact moment a decision gets made.
How do you discover the real prompts customers ask AI?
You do not invent prompts at a whiteboard. You harvest them from places where customers already speak in their own words. Here is the sequence we run, and you can start it this week:
- Pull your last 30 sales calls and support tickets. Read for the moment a customer describes their problem before anyone corrects their language. That raw phrasing — "can this thing connect to our old system" — is a prompt.
- Export your on-site search queries. Whatever people type into your website's own search bar is a prompt they'd happily type into ChatGPT.
- Mine reviews and community threads. Reddit, industry forums, G2, and product reviews are full of comparison questions phrased exactly the way buyers think.
- Ask each customer-facing team to log the questions they answer most. Record the sentence, not a summary of it.
- Layer in keyword data last, not first. Traditional keyword tools show demand, but they flatten the question. Use them to confirm scale, not to write the prompt.
Then expand every question along real variables: industry, location, budget, goal, and experience level. "Best CRM" becomes "best CRM for a 12-person construction firm under $100 a month." The expanded version is what people actually ask, and it is far easier to get cited for.
Finally, test the prompts across several AI platforms — ChatGPT, Gemini, Perplexity, and Google's AI Overviews — and note which brands and sources appear. That test is the whole point: it shows you where you already show up and where a competitor owns the answer.
What not to do: do not treat prompt research as a one-time list you generate and forget. The questions drift as the category matures. We re-test client prompt sets monthly, because the answer AI gave in January is often not the answer it gives in June.
Informational vs. transactional prompts: which ones actually convert?
Not every prompt is worth chasing. Informational prompts — "what is GEO" — build awareness but rarely move a buyer. Transactional and comparison prompts — "which GEO platform is best for a small marketing team" — sit closer to a decision, and those are where a citation turns into a lead.
The practical rule we use: if the prompt contains a constraint (budget, team size, industry, deadline), it is closer to money. Sort your library so the constraint-heavy prompts sit at the top of your content roadmap. That's the same logic behind why shopping-related prompts jumped 25% in Bain's Sensor Tower analysis — people bring specifics to AI when they are ready to act.
Building a structured prompt library, not a random list
A pile of 200 questions in a spreadsheet is not research — it's noise. A prompt library is organized by intent, topic cluster, and buying stage, with each prompt tagged for the variable that makes it specific. That structure is what lets you turn research into a content plan instead of a guessing game.
Structure also improves the output on the AI side. One analysis found that structured prompt processes reduce AI errors by up to 76%, per SQ Magazine — the same discipline that makes an AI answer cleaner makes your research usable. When Grid13 generates content, we map each article to a cluster of related prompts so a single post can satisfy several real questions at once, which is exactly how AI engines pull answers together.
The investment behind this is not trivial, and buyers are backing it. According to the State of AI Customer Research report, the median research-led organization spent $84,000 on AI customer research tooling in 2025, up from $20,000 in 2024 — a 4.2x increase. That's a signal that understanding how customers talk to AI has moved from experiment to budget line.
Why prompt research beats a keyword-only strategy in 2026
Keyword-only strategies were built for a world where the search result was a list of ten blue links. That world is shrinking. The same State of AI Customer Research data shows UX research adoption of AI jumping from 38% in 2024 to 73% in 2026 — teams are moving fast toward tools that read intent, not just terms.
Adoption is broad across functions, too. Gartner reported that 85% of customer service leaders will explore or pilot a customer-facing conversational GenAI solution in 2025. Every one of those deployments generates a fresh stream of real customer prompts — a source of research most companies are not yet mining.
Prompt research is not a replacement for keyword research; it's the layer above it. Keywords tell you the topic has demand. Prompts tell you the exact conversation, so you can write the answer AI will quote. Grid13, which builds AI-generated blog content designed to rank on Google and get cited by AI search engines, treats prompt research as the first step in every content pipeline — because the questions decide everything that follows. You can see how that works on the Grid13 platform.
Frequently Asked Questions
What is prompt research in plain terms?
It's finding the full questions people ask AI tools before they decide to buy, then organizing them so you can write content that answers them. Think of it as keyword research updated for a world where customers talk to AI in complete sentences instead of typing three words into Google.
Where do I find the prompts my customers actually use?
Start with sources where customers already speak freely: recorded sales calls, support tickets, your site's own search bar, product reviews, and community forums. Log the exact wording rather than a tidy summary — the messy original phrasing is what people type into AI.
How is prompt research different from keyword research?
Keyword research captures short terms with no context; prompt research captures whole questions with a budget, industry, or goal attached. Both matter, but a keyword tells you a topic has demand while a prompt tells you the precise conversation to write for.
Do informational or transactional prompts matter more?
Both, but transactional and comparison prompts convert better because the person is closer to a decision. A quick test: if the question contains a constraint like price, team size, or a deadline, prioritize it — those specifics signal buying intent.
How often should I update my prompt library?
Re-test it monthly. AI answers shift as a category matures, and a prompt that named you in one month can name a competitor the next. Regular re-testing across ChatGPT, Gemini, Perplexity, and Google's AI Overviews keeps your content aligned with what AI is actually saying.