Prompt Discovery

Where keyword research maps the search terms people type, prompt discovery maps the full, conversational questions people ask an assistant: "what's the best X for Y," "is brand Z worth it," "which of these should I buy for…." These prompts are longer, more intent-rich, and often bundle several constraints, so the discovery process has to think in questions and buyer scenarios, not single keywords.

Good prompt discovery spans the buying journey and the competitive field: category and comparison questions, product-fit and objection questions, use-case and gifting questions, and branded questions about you and your rivals. The output is a structured prompt set, the questions that, if you win them, move revenue, which becomes the input to prompt tracking.

Discovery is ongoing, not one-time. New products, seasons and competitor moves create new prompts, and the way people phrase questions to AI keeps shifting. Treating the prompt set as a living list, pruned and extended over time, is what keeps a GEO program aimed at questions that still matter.

For a store, prompt discovery is what stops GEO from being guesswork. Optimizing random pages for imagined questions wastes effort; discovering the specific prompts buyers actually use tells you which pages to fix and which answers to win, in priority order.

It also sizes the opportunity honestly. Seeing the real question set, and which prompts you already win, lose, or share with competitors, turns "we should do something about AI" into a concrete, prioritized backlog tied to buyer intent.

Illustrative scenario: a skincare store runs prompt discovery and finds buyers asking "what's good for sensitive skin that's also fragrance-free" and "is mineral or chemical sunscreen better." Those become tracked prompts, and the pages that should answer them become the first optimization targets.