How themes, intent categories, branded controls, and unbranded discovery prompts create a defensible measurement input layer.
A prompt library is not a keyword list
Generative interfaces are conversational and intent-rich. Prompts should represent questions users might realistically ask rather than mechanically converting keywords into sentences.
Cover the major information tasks
Kumar groups prompts into discovery, problem/solution, use case, comparison, expert, and brand research. Discovery measures broad category emergence; problem and use-case prompts test specific associations; comparison and brand research often contain names; expert prompts test high-information contexts.
Organize prompts into business themes
A hierarchy from business goal to theme, category, and prompt makes topical coverage measurable. A running-shoe library might separate beginner running, flat feet, marathon, and daily training rather than track unrelated questions.
Separate branded recognition from unbranded discovery
“Is Klaviyo good for ecommerce?” supplies the entity. “Best email marketing platform for ecommerce” tests whether the engine surfaces it independently. Mixing them can inflate visibility through near-saturated named recognition.
Report Branded Recognition Rate and Unbranded Discovery Rate separately.
Questions about this topic
How is a prompt library different from a keyword list?+
It models realistic conversational information needs, contexts, constraints, and decision tasks.
Which prompt categories should be included?+
Discovery, problem/solution, use case, comparison, expert, and brand research are a useful starting taxonomy.
What is a prompt theme?+
A group of related prompts representing one topic, audience, problem, use case, or business objective.
Why keep branded and unbranded prompts separate?+
Named recognition is much easier than independently emerging in an unbranded category or problem answer.
References
Sources are listed in APA 7 style. Preprints are identified as such and should not be treated as peer-reviewed findings unless separately published.
- Kumar, P. (2026). Generative engine optimization at scale: Measuring brand visibility across AI search engines [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2606.20065
- Martinez, O. (2026). Optimizing visibility in generative engines: A critical survey of generative engine optimization (2023–2026) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.14035