Definition
Prompt Research is the process of discovering, organizing, and prioritizing the prompts (questions and queries) that users type into AI search assistants like ChatGPT, Perplexity, and Google AI Overviews. It is the GEO equivalent of keyword research: without knowing which prompts matter, you cannot measure or improve AI search visibility.
Prompt research is harder than keyword research because AI platforms do not publish prompt volume data. You build a prompt library from real user intent: customer interviews, support tickets, sales calls, search console queries, competitor content, and latent intent analysis. Our prompt research guide walks through the framework.
A good prompt library is organized by topic, intent stage, and persona, and tracked across models. Prompt volume estimates help prioritize, and prompt monitoring keeps the library current as behavior shifts. Use the prompt tracking feature to run the library across ChatGPT, Claude, Perplexity, and Gemini and measure citation share and brand inclusion rate per prompt.
Refresh prompt research quarterly. AI behavior changes fast, and a prompt library built once goes stale as new models, features, and query patterns emerge.
Examples of Prompt Research
- A brand builds a prompt library from sales calls and support tickets and discovers buyers ask ChatGPT comparison questions the brand's content never answers.
- A GEO team uses the [prompt research guide](/blog/find-the-right-queries-to-track) to build a 300-prompt library organized by persona and intent stage.
- A team prioritizes prompts by estimated [prompt volume](/glossary/prompt-volume) and tracks [citation share](/glossary/citation-share) per prompt via [prompt tracking](/features/prompt-tracking).
- A content team refreshes its prompt library quarterly and finds new agentic-search phrasings absent from last quarter's set.
