How to find the prompts that matter for ChatGPT and Claude visibility
How to find the AI prompts that actually move pipeline. A buyer-journey method for picking the ChatGPT and Claude queries worth optimizing for.
Originally published January 11, 2025
Reviewed and updated for 2026.
Most AI visibility programs start too broadly. Teams test hundreds of vague prompts and learn almost nothing. The better approach is to identify the prompts that sit closest to real purchase decisions, then defend visibility there.
Soar is a community marketing agency that has run 4,200+ community campaigns across 280+ brands since 2017. The method below is the one we use to scope an AI visibility engagement before any content or community work begins, because the cost of optimizing for the wrong prompts is months of effort with no commercial signal.
Start with the buyer journey
The prompts that matter usually fall into three groups.
Discovery prompts: the buyer is framing the problem. ("How do we measure community ROI?")
Comparison prompts: the buyer is narrowing options. ("Best Reddit marketing agencies for B2B SaaS.")
Decision prompts: the buyer is asking for a recommendation or a final check. ("Is X agency good for a $50k engagement?")
If your brand is visible across all three, you build presence across the decision path. If you are only visible in discovery prompts, the commercial impact is limited because comparison and decision prompts are where the shortlist is built.
Use customer language, not internal language
The best prompts come from sales calls, support tickets, paid search queries, community threads, and inbound email questions. These sources reveal the exact words buyers use when they describe a need.
That matters because LLMs respond to semantic framing. The category labels your team prefers — "AI visibility platform," "GEO program," "community marketing engagement" — may not be the language your buyers actually type. The fastest way to find the gap is to read 30 inbound replies in a row and circle the verbs and nouns the customer used.
Map competitor presence
Run the most important prompts across ChatGPT and Claude, plus Perplexity and Google's AI Mode if those matter for your category. Note which brands show up consistently and which rotate.
A useful read on the result:
Two or three competitors appear consistently → high-value prompt, defended space, worth a structured campaign.
One competitor dominates → likely a defensible category claim; you need a wedge, not a head-on push.
No credible brand appears → either an emerging space worth seeding early, or a low-intent query the model answers from generic content.
Cluster similar prompts before testing
Different prompts often resolve to the same answer set. Grouping them into clusters makes the work efficient. Instead of treating every phrasing as a separate battle, treat prompt clusters as shared intent spaces and prioritize the content, citations, and external signals likely to influence the whole cluster at once.
A practical clustering rule: if two prompts return three or more of the same brands in the top results, they belong in one cluster. Optimize for the cluster, not the wording.
Re-test on a cadence
AI answers are probabilistic and drift over time. The prompt set that matters for your category should be tested at least monthly, with deeper audits on every major model release. Track which prompts mention your brand, which competitors dominate, the citations the models use to support each answer, and how the language of the answer itself changes.
That history is also the only honest way to attribute community and content investments to AI visibility outcomes — without it, every uptick gets credited to the most recent thing the team shipped.
Common mistakes
Testing the brand prompt only. "What is [your brand]?" is a vanity check. Decision prompts that don't mention your brand are the harder, more valuable target.
Optimizing for one model. ChatGPT and Claude weight sources differently, and Perplexity is closer to a search engine. A prompt set that ignores the model split misses where you are actually losing.
Stopping at the prompt list. A prompt list with no owner, no cadence, and no link to commercial outcomes is a slide, not a program.
Frequently asked questions
How many prompts should an AI visibility program track?
Most B2B brands land between 30 and 80 active prompts after clustering. Fewer than 30 usually means the buyer journey isn't fully mapped; more than 80 usually means duplicate phrasings that should have been collapsed.
How often should the prompt set be re-tested?
Monthly at minimum, weekly for prompts where you are actively running community or content campaigns, and a fresh full audit on every major model release.
Can the same prompt set work across ChatGPT, Claude, and Perplexity?
The intent set carries across; the prompt phrasings sometimes don't. ChatGPT and Claude both reward natural-language queries, but the citations they pull from differ enough that you should track each platform separately.
What signals influence whether your brand shows up?
Consistency of brand mentions across high-authority sources (Reddit, Quora, news, industry publications), citation density on the brand's own site, and entity coherence across the open web. The community side of that mix is where most brands underinvest.
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