Blog · Prompt research for AI search
Prompt Research: How to Discover the Questions Buyers Ask AI
Priya Bothra · September 8, 2026
Prompt research is the process of finding and organizing the questions your buyers ask AI assistants, so you know which answers you need to win. You do it by collecting real buyer language from sales calls, support tickets, reviews, communities and search data, turning it into realistic prompts with the context buyers include, grouping them by intent and prioritizing them by business value and visibility gap.
Prompt research replaces keyword research as the starting point for AI visibility work, although the two overlap. Keywords tell you what people type into a search box. Prompts tell you how they ask an assistant, which usually includes their situation, constraints and preferences. That context decides which brands get recommended.
How prompts differ from keywords
| Keywords | Prompts | |
|---|---|---|
| Typical length | A few words | A sentence or several |
| Context | Minimal | Situation, constraints, preferences |
| Example | "payroll software" | "What payroll software works for a 12-person startup with contractors in three countries?" |
| Data availability | Search volume tools | No public volume data for most assistants |
| Result | Ranked list of pages | Synthesized answer, often a shortlist |
Longer, more specific questions also trigger AI answers more often in Google. Ahrefs' 2026 benchmark reported that AI Overviews appeared on 46.4% of queries with seven or more words, compared with 9.5% of single-word queries. The Pew Research Center similarly found that longer and question-based searches were more likely to produce an AI summary.
One important caveat: major AI assistants do not publish prompt volume data. Prompt research relies on proxies and first-party evidence rather than exact demand numbers. Be skeptical of any tool that claims precise prompt volumes without explaining its method.
Where to find real buyer questions
Sales conversations
Call recordings and notes contain the questions buyers ask before they buy, in their own words. Look for comparison questions, objections and "does it work for..." questions.
Support tickets and onboarding questions
These reveal use-case and capability questions, especially from buyers who were unsure whether the product fit.
Reviews
Your reviews and competitors' reviews show the problems buyers were solving and the criteria they used to choose.
Communities
Forums, Reddit threads, Slack and Discord groups, and Q&A sites show how buyers ask peers for recommendations. These are especially relevant because community platforms are heavily cited by some assistants. Profound's citation analysis found Reddit was the top cited domain for Perplexity and Google AI Overviews.
Search data
Search Console queries, "People also ask" questions and long-tail keyword data remain useful proxies for the topics people care about, especially question-form queries.
The assistants themselves
Ask AI assistants what questions buyers typically ask about your category, and look at the follow-up suggestions some interfaces offer. Treat these as idea generators, not demand data.
Step-by-step prompt research process
Step 1: Collect raw questions
Aim for 100 to 300 raw questions from the sources above. Keep the original wording.
Step 2: Rewrite them as realistic prompts
Turn each question into how a buyer would ask an assistant, including context. "Best CRM" becomes "What's the best CRM for a 10-person B2B agency that already uses Google Workspace and needs simple pipeline tracking?"
Step 3: Tag each prompt
Tag by:
- Intent: discovery, comparison, alternatives, use case, pricing, implementation, trust.
- Buying stage: problem-aware, solution-aware, vendor evaluation, purchase, post-purchase.
- Persona: the role asking.
- Constraints: size, budget, industry, integrations, region.
Step 4: Group into clusters
Group prompts that share the same underlying need. Five to ten clusters is typical for a focused product. Each cluster will map to one or more pages or content assets.
Step 5: Consider query fan-out
Google describes AI Overviews and AI Mode as issuing multiple related searches across subtopics. For each cluster, list the sub-questions an AI system is likely to research, such as pricing, integrations, security and alternatives. Your content should cover those sub-questions too.
Step 6: Test current visibility
Run a sample of prompts from each cluster repeatedly across the assistants that matter and record visibility, recommendations and cited sources.
Step 7: Prioritize
Score clusters on three factors: business value, current visibility gap and your ability to win. A simple 1-to-3 score for each works well.
| Cluster | Business value | Visibility gap | Ability to win | Priority |
|---|---|---|---|---|
| Comparison vs top competitor | 3 | 3 | 2 | High |
| Pricing questions | 3 | 2 | 3 | High |
| Broad category discovery | 2 | 3 | 1 | Medium |
| Implementation how-to | 1 | 1 | 3 | Low |
Illustrative scoring for a hypothetical company.
Step 8: Maintain the set
Add new prompts as products, competitors and buyer concerns change. Keep a stable core set for trend tracking and add new prompts as a separate group, so changes in the prompt set do not distort your trends.
Turning prompt research into content
Each priority cluster should have:
- A page that answers the core question directly in the first sentences.
- Sections covering likely fan-out sub-questions.
- Specifics buyers need: pricing ranges, integrations, limitations and ideal customer.
- A plan for third-party coverage in the sources models cite for that cluster.
Common mistakes
Inventing prompts in a meeting. Internal guesses reflect how your team talks, not how buyers ask.
Dropping the context. Removing constraints turns prompts back into keywords and hides who wins specific buyer situations.
Trusting unexplained volume numbers. Without published prompt data, treat volume estimates with caution.
Too many prompts, too few runs. Fifty prompts run five times each tells you more than 500 prompts run once.
Never updating. Buyer questions shift with new competitors, regulations and technology.
A hypothetical example
A hypothetical e-signature company reviews 200 sales call notes and finds buyers repeatedly ask whether electronic signatures are valid for specific document types in specific countries. The company had been targeting "best e-signature software." After building a cluster around legal validity questions and testing it across assistants, it finds competitors are rarely cited either. The cluster becomes an open opportunity: a well-sourced guide to e-signature validity by country, reviewed by legal counsel, answers questions no one else answers well.
How Bob Builds AI helps
Bob Builds AI's Prompt Research uncovers the questions customers ask across AI platforms and analyzes each prompt for intent, competing brands and recommendation patterns, then surfaces the highest-impact conversations. Paired with Visibility Monitoring, it tracks how answers change over time.
FAQ
What is prompt research?
Prompt research is the process of discovering, organizing and prioritizing the questions buyers ask AI assistants like ChatGPT, Gemini and Perplexity. It uses real buyer language from sales, support, reviews, communities and search data to decide which AI answers a brand needs to win.
How is prompt research different from keyword research?
Keyword research focuses on short search terms with measurable volume. Prompt research focuses on longer, conversational questions that include buyer context and constraints. Prompts better reflect how AI assistants are used, but most assistants do not publish prompt volume data.
Is there search volume data for ChatGPT prompts?
Not publicly from the major AI providers. Some tools estimate prompt demand using panels or modeling, so ask how estimates are produced. First-party evidence from sales, support and search data is usually more reliable for prioritization.
How many prompts should I track?
Most teams track 30 to 100 prompts in five to ten clusters, running each repeatedly across the assistants their buyers use. Fewer, well-chosen prompts with repeated runs produce more reliable insights than large sets run once.
Where can I find the questions customers ask AI?
The best sources are sales call recordings, support tickets, reviews of your product and competitors, community discussions, Search Console queries and "People also ask" results. AI assistants can suggest ideas, but real buyer language should anchor your prompt set.
How often should prompt research be updated?
Review your prompt set quarterly and update it after product launches, pricing changes, new competitor entries or shifts in regulation. Keep a stable core set for trend comparisons.
What should I do after prompt research?
Test current visibility for each prompt cluster, prioritize clusters by business value and gap, then create answer-first content and pursue third-party coverage for the highest-priority clusters. Re-test regularly to measure progress.
Conclusion
Prompt research tells you which questions matter before you write anything. By collecting real buyer language, keeping the context buyers include, grouping prompts by intent and prioritizing by value and gap, you focus AI visibility work where it can influence decisions.
A good first step is to pull the last 50 sales call notes and extract every question a buyer asked. You will likely find clusters that no competitor answers well. Bob Builds AI's Prompt Research can help scale that process across AI platforms.