How to handle recommended prompts and confirmation

Ask & Discuss
1

Beamtrace gives you inital prompts to be monitored and ranked. You can add new once or confirm them.
If I look on the onces provided by Beamtrace they are very generic and do not reflect a natural search or wording by a person prompting. If I change them to more natural prompts that I believe are more common they obviously will impact the report and visibility KPI.
My question is: How do I know the real prompts that people use? It is similar to keywords (SEO) to research them (like google search console)?
Where is a good source to check what people are really prompting?
Are the recommended prompts from Beamtrace good enough and where did they actually come from?
If someone has more information and guidance on that it would be much apprechiated.
Thanks
Thomas

2

Here is the AI-answer that I found myself to my question that is already very useful:

Unlike traditional SEO, there is no direct equivalent to Google Search Console for AI platforms. OpenAI, Anthropic, and Perplexity do not release query volumes or exact user prompt data due to strict privacy constraints surrounding conversational chat logs.

Are the recommended prompts from Beamtrace good enough?

Most AI visibility tools generate their recommended seed prompts programmatically. The platform scans a domain and uses an LLM to generate questions a user might ask about that business or industry. Because these prompts are synthetically generated, they often feel robotic or align too perfectly with a brand’s own marketing copy, rather than reflecting how a human actually types.

They are a baseline starting point, but they are rarely good enough on their own. You are absolutely correct to rewrite them. While changing them will shift your visibility KPI, tracking synthetic prompts that no human actually uses gives a false sense of security. Adjusting them to match natural, conversational phrasing ensures your Generative Engine Optimization (GEO) efforts are anchored in reality.

Where to research real user prompts

Since there is no centralized database of AI search volumes, you have to proxy human conversational intent using other data sources:

  • Long-Tail Search Console Data: Filter existing Google Search Console data for question modifiers (how, what, best, vs) and queries containing 5 or more words. Long-tail, highly specific Google searches closely mirror how people prompt AI assistants.

  • Perplexity “Related” Questions: Type a core topic into Perplexity. The “Related” follow-up questions generated at the bottom of its answers are heavily informed by real user query patterns and conversational flows.

  • Intent Mapping Tools: Platforms like AnswerThePublic or AlsoAsked scrape search engine question ecosystems. The specific question structures they uncover translate perfectly to AI prompting behavior.

  • Direct Customer Language: The most accurate source of natural language is internal. Analyze the exact phrasing clients use in emails, contact forms, or support tickets when describing their problems or requesting services.

  • Reddit and Niche Forums: Users increasingly rely on AI to replace forum searches. Looking at how users format recommendation requests on Reddit provides the exact templates people use for LLM prompts (e.g., detailing specific constraints, locations, and requirements).

4

Hi Thomas,

Thanks for following up and sharing what you found!

The AI-generated answer looks broadly reasonable to me, and I’d agree with the general idea that Beamtrace’s suggested prompts can be treated as a starting point rather than a fixed list you shouldn’t change.

Refining them to better match the way your actual customers would naturally ask questions makes sense. And, as you mentioned, it’s worth keeping in mind that changing the prompts can also affect the visibility metrics, since you’ll then be monitoring a different set of queries.

I’d just be a little careful about taking all the details in the AI answer as definitive, particularly when it comes to how Beamtrace generates its recommendations and which sources best reflect real AI prompts.

I’ve asked our team about exactly that and am still waiting for their feedback. Once I hear back, I’ll share any additional insights or recommendations here :slightly_smiling_face:

Thanks again for bringing this up!

5

Hi @thomas_john_martin :waving_hand:

Thank you for waiting!

Our devs reviewed the AI answer you’ve received and all the details and recommendations are correct.

If you have any additional questions, feel free to contact us here. We’ll be happy to advise :slightly_smiling_face: