Your GSC Data Already Knows Which AI Prompts You're Visible For

I got tired of watching the same scene play out. SEO team builds a prompt list. AI visibility platform starts tracking. First report shows 3% visibility. Stakeholders panic.

Here’s what I realized after months of reverse-engineering ChatGPT and Perplexity’s citation systems: your Google Search Console data is a map of your probable AI visibility.

Think about it. You rank for thousands of queries in GSC. Real users typed those queries. Those same users are now asking ChatGPT similar questions, just phrased conversationally instead of in keyword fragments.

“best crm small business” in Google becomes “What’s the best CRM for a small business?” in ChatGPT.

If you rank first, you probably get cited for the second. But nobody’s checking.

Instead, teams brainstorm prompts from scratch, track aspirational targets, and wonder why their visibility reports look empty.

So I Built Something

A tool that takes your existing search query data and transforms it into AI prompts worth tracking.

How it works:

1. Export your ranking queries from GSC, Bing Webmaster Tools, Semrush, Ahrefs, whatever you use

2. The tool uses Flash 2.5 to convert keyword-style queries into natural conversational prompts

3. BrightData’s ChatGPT scraper validates which prompts actually return citations to your domain

4. You get a list of prompts where you’re already visible, not guesses, not hopes, actual baseline data. Note: You won’t get 100% score but meaningful results.

The system prompt handling the conversion is the key piece. It’s not just “rewrite as a question.” It understands how LLM users phrase things differently than Google users. It generates multiple variations. It maintains intent while matching interaction patterns.

Why This Changes the Conversation

Old way: “We’re tracking 100 prompts. We’re visible in 3.” Stakeholder concern intensifies.

New way: “I analyzed our top GSC queries and found 47 prompts where ChatGPT already cites us. Here’s our baseline. Here’s the expansion strategy.” Stakeholder nods approvingly.

You’re starting from strength. You’re showing existing value. You’re grounded in data instead of guesswork.

This matters because AI visibility is probabilistic. Citations fluctuate. But starting with prompts where you have a foundation makes everything more concrete.

The Caveats (Read These)

I’m not going to oversell this.

It’s not 100% accurate. The tool finds closer structures of prompts, semantic matches, not guarantees. AI responses shift. This gives you direction, not precision.

You need real data. Minimum 1,000 impressions and 100+ queries in your GSC. Thin accounts won’t produce meaningful patterns.

GSC wins don’t guarantee LLM wins. This is the big one. LLMs have a reranking layer that reorganizes everything. Your GSC top performers can absolutely get demoted in the AI layer. GSC is being used as a semantic wrapper here.

The tool shows you where you probably appear. Confirming that visibility, stabilizing it, expanding it. That’s the real AEO work that comes after.

HOT TIP: Also use this RegEx in your GSC:

([^” “]*\s){15,}?

Get the Tool

Runs locally. Your data stays on your machine.

Requirements:

• Gemini API key (free tier available)

• BrightData account (paid, for ChatGPT scraping)

• Sufficient GSC data to work with

Download Link:

[Download] GSC to AI Prompts Tool

Setup instructions in the package.

Blog post link: https://metehan.ai/blog/stop-guessing-which-prompts-to-track/

If you’ve been struggling with the “what prompts should we track” question, or if your stakeholders keep asking why your AI visibility reports look empty, try this.

Start with what you already have. Your search data knows more than you think.

—Metehan

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P.S. If you’re working on AI visibility and want to talk strategy, reply to this email. Always happy to discuss what’s actually working.