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GEO Prompt Research: Find the Prompts Worth Optimizing For
Summary
Prompt research replaces keyword lists with a prioritized prompt map of the AI questions where a mention moves a purchase, because in generative search the unit that matters is the question, not the word.
2026/07/05
9 min read
GEO prompt research is how you find the conversational questions buyers actually type into ChatGPT, Gemini, and Perplexity, then decide which ones are worth monitoring and optimizing for. Instead of chasing exact-match keywords, you collect real prompts, sort them by intent, cluster them into themes, and prioritize the handful where an AI mention or citation can move a purchase. The deliverable is not a keyword list but a prompt map that tells your team which answers to win.
For two decades, search optimization ran on strings: "best running shoes," 50,000 searches a month, build a page to match. Generative engines dissolved that model. A buyer no longer types "CRM software" — they write something like, "I run a 10-person marketing agency and need a CRM that syncs with Slack and stays under $50 a month; what do you recommend?" The keyword is buried in a paragraph of context, and the engine replies with a shortlist instead of ten blue links. Prompt research is the discipline built for that reality, and it sits at the center of Generative Engine Optimization, a field first formalized in academic research in 2023.
Key takeaways
Prompt research trades search volume for intent and context: track the questions buyers ask AI, not the keywords they used to type into Google.
Four prompt types deserve separate plays — discovery, comparison, use-case, and grounding queries — because each is won differently.
A three-tier prompt map (brand defense, competitor conquest, category leadership) turns a scattered list into a prioritized roadmap.
Grounding queries — the searches an engine runs to fact-check itself — are where citations are earned, so put them in the map early.
Automate discovery and monitoring so you can cover hundreds of prompts across seven engines instead of hand-checking a dozen.
From strings to intent
Traditional keyword research optimizes for volume; prompt research optimizes for context. "CRM software" is a keyword with a knowable monthly search count. "Compare HubSpot and Salesforce for a startup focused on outbound sales" is a prompt with unknown frequency but extremely high commercial value. You cannot pull an exact-match volume for it, and that is the point. In generative search, the question that matters is whether your brand shows up in the answer and how prominently — captured by metrics like Share of Model and AIGVR rather than impressions and rank.
The practical consequence: stop scoring prompts by how many people type them and start scoring them by what happens when your brand is present versus absent. A prompt asked 40 times a month that ends in a purchase decision beats a 40,000-a-month prompt that never converts.
Search has informational and transactional queries; GEO has prompt types. Sorting your list this way tells you what "winning" even means for each entry.
Discovery prompts
Broad, top-of-funnel questions such as "What are the best project management tools in 2026?" The engine answers with a consideration set, usually three to five options, and your goal is simply to be in that set. If you are not named here, later comparison and use-case prompts rarely rescue you, because the buyer never learns you exist.
Comparison prompts
Middle-of-funnel head-to-heads: "Geoly vs Profound: which is better for enterprise GEO?" or "Notion alternatives for engineering teams." Here the engine weighs positioning, so winning depends on whether the model holds an accurate, well-sourced understanding of what makes you different. Vague or missing differentiation is where most brands lose.
Use-case prompts
Long-tail, specific questions like "Can I use Shopify to sell digital courses?" Volume per prompt is tiny, but intent is unusually high, and there are thousands of them. The goal is to be the direct answer, or the cited solution, to a concrete job.
Grounding queries
These are the searches the engine runs on itself. When a user asks Perplexity a question, the system fires several background searches to gather and verify sources before it writes the answer. You never see them, but ranking for these hidden queries is how you become a citation rather than a footnote. Treat them as their own category: the research the model does about the research the user asked for. To go deeper, see grounding queries and citation analysis. Google itself now publishes guidance on optimizing for its AI features.
A four-step method for finding prompts
1. Seed and expand
Start with your core product terms, then rewrite them as conversations. The fastest manual technique is to make an LLM roleplay your buyer: "Act as a [target persona]. List 20 questions you would ask an AI assistant while shopping for a [product category]. Include real constraints — budget, integrations, team size, industry." You will get natural-language prompts no keyword tool would surface.
2. Mine follow-up queries
After answering, most engines suggest related or follow-up questions. Those suggestions are the model's own guess at the next logical step in the journey, which makes them free intent data. Ask a seed question in Perplexity or ChatGPT, read the "Related" block, and add anything relevant to your list. Repeat one level deeper and you have a branching map of the real conversation.
3. Competitor truth-gap mining
Ask engines about a competitor's pricing, limits, or specific features, then check the answer against reality. Models frequently repeat outdated or wrong details. Each error is a truth gap: publish clear, well-structured, current content on that exact point and you have a strong shot at becoming the source that corrects it. This doubles as competitive intelligence on how AI currently frames the category.
4. Automate discovery at scale
Hand research caps out around a few dozen prompts, which is nowhere near a category. Platforms built for this generate and cluster prompts programmatically. In GEOly AI, the workflow runs end to end: you enter a few seed terms, the system expands them against live search data, a prompts agent rewrites that data into thousands of realistic natural-language prompts, and everything is clustered by topic — pricing, integrations, alternatives, use cases — so you can prioritize themes instead of drowning in a flat list.
Query fan-out tracking: how ChatGPT expands buyer questions into web search queries, with popular searches and demand themes — Source: GEOly AI (app.geoly.ai)
Build a three-tier prompt map
A prompt map is the strategic layer: the specific questions you intend to own in AI answers, ranked by how much winning them is worth. A simple three-tier structure keeps it honest.
Tier 1 — brand defense (must win)
Prompts: "What is [your brand]?", "Is [your brand] legit?", "[your brand] reviews." When someone already knows your name, the AI's answer becomes your first impression. Target complete accuracy and positive sentiment; a single hallucinated fact here costs you deals you never see.
Tier 2 — competitor conquest (high value)
Prompts: "[competitor] alternatives," "[competitor] vs [your brand]." These reach buyers already in-market who are simply choosing between vendors. The goal is to be the top recommended alternative, backed by clear, sourced differentiation the model can quote.
Tier 3 — category leadership (volume)
Prompts: "best [category] tools," "how to [problem you solve]." This is the widest, most competitive tier, and the hardest to move. Aim for consistent top-three inclusion in the lists engines generate; even partial presence compounds, because discovery prompts feed everything downstream.
Monitor, don't set and forget
A prompt map is a snapshot, and models change their answers constantly — sometimes day to day, sometimes after a single index refresh. Monitoring turns the map into a live scoreboard. Track your prompt set across all seven major engines — ChatGPT, Gemini, Perplexity, Copilot, Grok, Google AI Mode, and AI Overviews — rather than spot-checking one. Roll the results into a single visibility score so trend lines are legible to people who will never open a raw transcript, and watch Share of Model to see how often you appear against the same competitors in the same prompts. When a tier-1 prompt slips, or a competitor overtakes you on a tier-2 head-to-head, that is your signal to act.
Share of Voice and Visibility Score benchmarking a brand against competitors in AI answers — Source: GEOly AI (app.geoly.ai)
To keep the loop closed, pair monitoring with a periodic GEO audit so you can trace a visibility drop back to a fixable cause — a stale source, a missing comparison page, an unclaimed product card. You can explore the full prompt-to-monitoring workflow in the app on a free trial, or compare plans on the pricing page.
Prompt research, in the end, is a shift in the unit of analysis. The old unit was the word; the new unit is the question, with all its context intact. Move from a keyword list to a prompt map and you stop guessing which strings buyers might type and start managing the actual conversations that decide who gets recommended. Keep going with more GEO playbooks or the broader AI search coverage.
FAQ
How is prompt research different from keyword research?
Keyword research ranks strings by search volume and matches pages to them. Prompt research collects full, conversational questions, sorts them by intent, and prioritizes the ones where an AI mention changes a buying decision, regardless of how often they are asked. The output is a prompt map, not a keyword list, and success is measured by AI visibility metrics like Share of Model rather than rank.
Can I do prompt research without a paid tool?
Yes, for a small set. Roleplay your buyer with an LLM to generate seed questions, mine the follow-up suggestions engines show after each answer, and check competitor facts for truth gaps. That is enough to build a starter map of a few dozen prompts. Automated platforms matter once you need to cover an entire category and monitor it across every engine on an ongoing basis.
What are grounding queries and why do they matter?
Grounding queries are the background searches an engine runs to gather and verify sources before it writes an answer. Users never see them, but they determine which pages get cited. Ranking for these hidden queries is often the difference between being quoted as a source and being left out, which is why they belong in your prompt map as their own category.
How many prompts should I actually monitor?
Start with the prompts that map directly to revenue: brand-defense terms, your top competitor comparisons, and the handful of category questions where inclusion drives real consideration. Twenty to fifty well-chosen prompts beat a thousand vanity ones. Expand the set as you confirm which themes convert, and prune prompts that never influence a decision.
