How to Turn Search Console and GA Keywords Into SightAI Prompts

You’ve been collecting keyword data for years. Now it’s time to put it to work in a completely new way.

Most firms sit on a goldmine of search data they’ve never thought to use for AI visibility. Your Google Analytics and Search Console accounts are full of the exact words and phrases real people use to find businesses like yours. That data, the keywords already driving traffic to your site, is one of the best starting points for building a SightAI prompt list that actually reflects how buyers search.

The problem is that keywords and prompts aren’t the same thing. Nobody types “commercial litigation attorney Denver” into ChatGPT. They type something like, “I need a litigation attorney in Denver who handles contract disputes for mid-size companies. Who’s good?” But the intent behind that keyword and that prompt? Nearly identical.

This post will show you how to export your keyword data, feed it to AI, and turn it into a prompt list you can track in SightAI in about 30 minutes.

Why your keyword data is the right starting point

Your Search Console and GA data tells you something no brainstorm can: what real people actually searched before they found you. These aren’t hypothetical queries. They’re the words buyers used when they had a real need and started looking for answers.

That makes them the perfect raw material for building AI prompts because:

• They reflect real intent, not guesses about intent
• They show you which services, topics, and pain points drive the most interest
• They reveal geographic and industry patterns you might not have thought to include
• They give you volume data so you can prioritize the prompts that matter most

The trick is turning those short, keyword-style queries into the natural-language prompts that people actually type into AI tools. That’s where AI comes in.

Step 1: Export your keyword data from Search Console

Google Search Console is the better source for this exercise because it shows you the actual queries people typed into Google before clicking through to your site. GA4 is useful for supplemental context, but Search Console gives you the raw search terms.

Here’s how to pull the export:

1. Go to Google Search Console
2. Click Search Results in the left sidebar under Performance
3. Set your date range — the last 6 months gives you a solid sample without too much noise
4. If you serve multiple verticals, use the Page filter to isolate keywords for specific service pages. This will make your prompt list more focused later.
5. Click Export in the top right and choose CSV

You’ll get a spreadsheet with columns for query, clicks, impressions, CTR, and position. The columns that matter most for this exercise are query and impressions. Clicks tell you what’s working in traditional search. Impressions tell you what buyers are searching for which is what we care about for AI prompts.

Optional but helpful: If you also want to pull data from GA4, go to Reports > Acquisition > Traffic Acquisition, filter by organic search, and export. The landing page data can help you understand which service areas are attracting the most search interest.

Step 2: Clean up the CSV (just a little)

Before you upload your keyword data to AI, spend five minutes cleaning it up. You’re not looking for perfection. Just removing noise so the AI focuses on the right patterns.

Open the CSV and:

• Delete branded queries. Remove any row that contains your firm name or your attorneys’/team members’ names. You want to focus on non-branded search intent. A.K.A., the people who don’t know you yet.
• Delete junk queries. Remove anything clearly irrelevant. Misspellings that lead nowhere, queries about topics you don’t cover, or queries with fewer than 5 impressions.
• Sort by impressions (high to low). This puts the highest-demand queries at the top, which helps the AI prioritize.
• Keep it to 100–200 rows. If you have thousands of keywords, trim to the top 100–200 by impressions. That’s more than enough for the AI to work with.

Save the cleaned file. You’re ready to upload.

Step 3: Upload the CSV and ask AI to find the patterns

Now open ChatGPT, Claude, or Gemini Advanced. Upload your cleaned CSV and use this prompt:

I’m uploading a CSV of search queries from Google Search Console. These are the keywords real people used to find our website over the last 6 months. The “query” column contains the search terms and the “impressions” column shows search volume.

Analyze this keyword data and identify: 1. The top 10–15 themes or intent clusters (group similar keywords together by the underlying need or topic they represent) 2. For each cluster, the approximate total impressions 3. Any notable geographic, industry, or service-specific patterns

Keep your analysis concise. Use plain language for the cluster names — name them by what the searcher was trying to accomplish, not by marketing jargon.

This gives you a structured view of what your actual buyers care about, organized by intent rather than individual keywords. You’ll probably see clusters like “finding litigation help in [city],” “comparing service options for [problem],” or “understanding costs for [service].”

Review the clusters. Do they match your understanding of your business? If the AI missed something important or grouped things oddly, tell it to adjust.

Step 4: Turn those keyword clusters into buyer-intent prompts

Now comes the key step. In the same conversation (so the AI still has your data loaded), use this prompt:

Using the keyword clusters you just identified, generate 20 realistic AI search prompts that a buyer with this intent would type into ChatGPT, Gemini, or Perplexity.

Rules: – Write each prompt in natural conversational language, the way a real person talks to an AI assistant – Include buyer context where the keywords suggest it (role, company size, geography, industry, specific problem) – Do not include any brand names in the prompts – Each prompt should be 10–25 words – Prioritize the clusters with the highest impressions — these represent the highest-demand topics – Spread the prompts across buying stages: discovery (just exploring), shortlist (actively looking for options), comparison (evaluating specific providers), and problem-specific (searching based on a pain point)

For each prompt, include: – The prompt itself – Which keyword cluster it came from – The buying stage it represents – The approximate impression volume behind that cluster

The AI will take your real keyword data and translate it into the natural language prompts that those same buyers would use in an AI tool instead of Google. The impression data helps you prioritize. Prompts based on high-volume keyword clusters are worth tracking first.

Here’s what makes this approach powerful:

You’re not guessing what buyers care about. You’re starting with proof of what they already search for and converting it into the format AI search actually uses. It bridges the gap between what you know works in Google and what you need to track in SightAI.

Step 5: Add the prompts your keyword data can’t show you

Keyword data is a strong foundation, but it has blind spots. There are buyer questions that don’t show up in Search Console because they’ve never been typed into Google. They only exist in AI conversations. These tend to be:

Comparison prompts — “How does [Competitor A] compare to [Competitor B] for [service]?” People rarely type this into Google, but they ask AI this constantly.

Scenario-based prompts — “We just lost a major client and need to rebuild our pipeline fast. What kind of marketing firm should we hire?” These are too conversational for traditional search but perfectly natural for AI.

Recommendation prompts — “Who are the best firms for [service] if you’re a [specific type of company]?” AI gets asked for recommendations in a way Google never did.

Add 3–5 prompts in these categories manually. Use what you hear in actual sales calls, intake forms, and client conversations. These won’t have impression data behind them, but they represent the new search behavior that SightAI is built to track.

Step 6: Test and finalize your prompt list

Before loading your prompts into SightAI, run each one through ChatGPT, Gemini, and Perplexity. For each prompt, check:

• Does AI return specific firm or company recommendations? If it gives generic advice instead of naming companies, rewrite the prompt to sound more like someone ready to hire, not someone doing research.
• Do your competitors appear? If yes, you’ve found a prompt worth tracking. If no one relevant shows up, the prompt might be too niche or oddly phrased.
• Do you appear? If yes, track it and protect that position. If not, track it and use it as a target for your optimization work.

Cut any prompt where AI consistently gives generic educational answers instead of recommendations. Those are useful content topics but not useful SightAI tracking prompts.

Step 7: Load, monitor, and refresh

Once you’ve tested and finalized your list, load your prompts into SightAI. Now your dashboard is tracking prompts that are rooted in real buyer behavior backed by actual search data from your own Google accounts.

Set a quarterly reminder to repeat this process:

• Pull a fresh Search Console export
• Look for new keyword clusters or shifts in volume
• Feed the updated data back into AI for new prompt suggestions
• Swap out prompts that are no longer relevant or no longer producing useful tracking data

Your keyword landscape changes. Your prompt list should change with it.

The 30-minute version

If you’re short on time, here’s the quick path:

1. 5 minutes: Export Search Console data (last 6 months, CSV)
2. 5 minutes: Clean the CSV — remove branded terms, sort by impressions, trim to top 150 rows
3. 5 minutes: Upload to ChatGPT/Claude and ask it to identify intent clusters
4. 10 minutes: Ask it to generate 20 buyer-intent prompts from those clusters
5. 5 minutes: Add 3–5 manual prompts for comparisons, scenarios, and recommendations that don’t show up in keyword data

That gives you a prompt list grounded in real data, built in half an hour, and ready to load into SightAI.

Quick-start checklist

• Exported Search Console queries from the last 6 months
• Removed branded and irrelevant queries from the CSV
• Uploaded the cleaned CSV to AI and identified intent clusters
• Generated 20 buyer-intent prompts based on those clusters
• Added 3–5 manual prompts for comparison, scenario, and recommendation queries
• Tested each prompt in at least one AI tool to confirm it returns competitive results
• Loaded the final prompt list into SightAI
• Set a quarterly reminder to refresh with new keyword data

Want a hand turning your keyword data into a SightAI prompt list? Book a 15-minute prompt review with our team and we’ll walk through it together.