How to Identify the Search Queries ChatGPT Sends to Google in 2026

How to Identify the Search Queries ChatGPT Sends to Google in 2026

Here's a problem I kept running into.

I'd write what I thought was a perfectly optimized blog post. Good keywords. Solid structure. It ranked decently on Google. But when I asked ChatGPT a question that should have surfaced my content? Nothing. My article was invisible.

The issue wasn't my content quality. The issue was that I was optimizing for the wrong queries.

When someone asks ChatGPT a question, it doesn't just pass that question to Google. It rewrites the question into multiple search queries--typically 3 to 5 of them--before searching. These are called fan-out queries. And if you don't know what they are, you're flying blind.

So I developed a methodology to reverse-engineer these queries. It's not perfect, but it's gotten me and my clients results. Here's the exact process.

Why Traditional Keyword Research Fails for AI Search

Traditional SEO taught us to find keywords people type into Google. Tools like Ahrefs and SEMrush give you search volume, difficulty scores, and related terms. That approach still works for Google rankings.

But AI search works differently.

People don't type keywords into ChatGPT. They ask questions. Conversational, messy, context-rich questions like:

"I work at a Seed funded B2B SaaS startup - I need to set up email marketing. What are some tools that I can look at?"

No one types that into Google. But plenty of people ask ChatGPT exactly that.

ChatGPT then transforms this conversational query into search-engine-friendly queries like:

• best email marketing tools for B2B SaaS startups 2026
• email marketing software B2B SaaS comparison
• affordable email marketing platforms for startups
• email marketing tools pricing comparison SaaS

These are the queries your content needs to rank for. Not the original user question. The fan-out queries.

The 5-Step Process to Find ChatGPT's Search Queries

Here's the methodology I use. It works whether you're optimizing for your own brand or doing this for clients.

Step 1: Build Your Keyword Universe

Start by listing every keyword relevant to your niche. I mean everything: primary keywords, secondary keywords, long-tail variations, competitor brand names, feature-specific terms.

Let's say you're an email marketing software. Your keyword universe might include:

• email marketing
• email marketing software
• email marketing SaaS
• B2B email marketing
• transactional email
• email automation
• drip campaigns
• email deliverability
• Mailchimp alternative
• email marketing for startups

Don't filter yet. Quantity matters here. You want 50-100 keywords minimum. Use traditional SEO tools, competitor analysis, and your own knowledge of the space.

Step 2: Transform Keywords Into User Queries

Now take those keywords and turn them into the questions real people ask ChatGPT.

This is where you need to think like your customer. What problems are they trying to solve? What context would they give? What constraints do they have?

From "email marketing for startups," you might generate:

• "What email marketing tool should I use for my early-stage startup?"
• "I'm a solo founder and need to set up email marketing on a budget. What are my options?"
• "We're a seed-funded B2B SaaS startup looking for email marketing tools. Recommendations?"
• "What's the best email marketing platform for a 10-person startup with limited technical resources?"

Notice how these queries include context that keywords alone don't capture: company stage, budget constraints, team size, technical ability. This context shapes the fan-out queries ChatGPT generates.

Step 3: Predict the Fan-Out Queries

This is the critical step. For each user query, predict the 3-5 search queries ChatGPT would generate.

Here's how ChatGPT thinks:

1. It extracts the core intent (finding email marketing tools)
2. It identifies qualifiers (B2B, SaaS, startup, seed-funded)
3. It adds commercial modifiers (best, top, comparison, reviews)
4. It appends the current year for freshness (2026) - which is why adding the year to your blog titles significantly boosts ChatGPT visibility

Let me walk through a real example.

User query: "I work at a Seed funded B2B SaaS startup - I need to set up email marketing. What are some tools that I can look at?"

Extracted keywords: Seed funded, B2B, SaaS startup, email marketing, tools

Predicted fan-out queries:

• best email marketing tools for B2B SaaS startups 2026
• email marketing software B2B SaaS comparison
• email marketing platforms for startups seed stage
• affordable email marketing tools for B2B SaaS
• email marketing tools pricing comparison SaaS

You can also ask ChatGPT directly to help generate these. Prompt it with: "If a user asked you [user query], what search queries would you use to find the answer?" It won't give you the exact queries it uses, but it gives you a reasonable approximation.

Step 4: Validate Against Actual SERPs

Now take your predicted fan-out queries and search them in Google. This is your reality check.

For each query, document:

• Which blogs and publications are ranking
• What their title tags look like
• How they've structured their content
• Which brands they mention

When I did this for the email marketing query, I found consistent patterns. Sites like Encharge, Mailtrap, EmailToolTester, and Sequenzy kept appearing across multiple fan-out queries. These weren't random - they had systematically created content targeting these exact search terms.

Here's a simplified version of what I found:

Fan-Out Query	Sites Ranking (Top 5)
best email marketing tools for B2B SaaS startups	Encharge, Mailtrap, Reddit, Aimers
email marketing software B2B SaaS comparison	EmailToolTester, Mailtrap, Encharge, Sequenzy
email marketing platforms for startups seed stage	Mailtrap, EmailToolTester, CampaignMonitor
early stage SaaS email marketing tools	Encharge, EmailToolTester, Aimers, Sequenzy, Loops

The sites that appeared across multiple fan-out queries were the same sites ChatGPT ended up citing in its answers. There's a direct correlation: more SERP coverage across fan-out queries equals higher likelihood of ChatGPT recommendation.

Step 5: Cross-Reference With ChatGPT Citations

The final validation step: ask ChatGPT the original user query and look at its sources.

ChatGPT includes a "Sources" section at the bottom of its answers. These URLs have a telltale signature - they include "?utm_source=chatgpt.com" in the link. This tells you exactly which pages ChatGPT read and cited.

Compare these sources against your SERP analysis. You should see significant overlap between pages ranking for your predicted fan-out queries and pages ChatGPT actually cites.

If there's a mismatch - if ChatGPT is citing sources that don't appear in your SERP research - it means your fan-out query predictions need refinement. Go back to Step 3 and adjust.

The Rise of Long-Form Fan-Out Queries

One thing I've noticed in recent months: fan-out queries are getting longer.

Early research showed ChatGPT's search queries averaged about 5 words. Simple, direct queries like "best email marketing software."

Recent analysis of 600+ queries shows the average has jumped to 15 words. Some queries hit 25+ words. These aren't just keywords anymore--they're complete questions packed with context.

Examples from actual ChatGPT searches:

• "best email marketing platforms for seed stage B2B SaaS startups with small teams limited budgets and simple setup"
• "email marketing tools for B2B SaaS companies including lifecycle emails onboarding drip campaigns newsletters and product triggered automation"
• "what email marketing tools do early stage B2B SaaS startups actually use reddit hacker news founder discussions"

The good news: if you optimize for the short-form fan-out queries, you'll likely capture the long-form ones too. The core keywords remain consistent. But it's worth noting that ChatGPT is increasingly searching for specific, context-rich information.

Tools and Templates for Fan-Out Query Research

Here's a practical framework you can use for any niche.

The Keyword-to-Query Matrix

Create a spreadsheet with these columns:

• Primary keyword
• User query (conversational)
• Fan-out query 1
• Fan-out query 2
• Fan-out query 3
• Top 3 SERP results for each
• ChatGPT citation (if applicable)

Fill this out for your top 20 keywords. You'll start seeing patterns--certain sites dominate across queries, certain title formats perform better, certain content structures get cited more often.

Fan-Out Query Modifiers

When generating fan-out queries, mix in these common modifiers:

• Commercial intent: best, top, comparison, vs, alternative, review
• Audience qualifiers: for startups, for enterprise, for small business, for B2B, for SaaS
• Feature-specific: with automation, with analytics, with integrations, with API
• Price-related: affordable, free, pricing, cost
• Time-based: 2026, latest, updated, new

How to Use Fan-Out Queries to Create Content

Once you've mapped out the fan-out queries, use them to guide content creation.

Match your title tag to the fan-out query exactly. If ChatGPT is searching "best email marketing tools for B2B SaaS startups 2026," your title should be "Best Email Marketing Tools for B2B SaaS Startups in 2026." Don't get creative. Match the query.

Create multiple pieces targeting the same user query. Remember, ChatGPT generates 3-5 fan-out queries. You want your brand appearing across multiple search results. Consider creating: a comparison post, a detailed guide, a listicle, a Reddit discussion, and a LinkedIn article.

Add fan-out queries as FAQ sections. Turn your predicted fan-out queries into FAQ questions on your main content piece. "What are the best email marketing tools for B2B SaaS startups?" answered directly on your page gives ChatGPT an easy chunk to extract.

Include the year everywhere. Title tag, H1, first paragraph, meta description. ChatGPT appends years to queries. Your content needs to signal freshness.

Frequently Asked Questions

What is a fan-out query in ChatGPT?

A fan-out query is a search query that ChatGPT generates when it needs to find information on the web. Instead of using your question directly, ChatGPT rewrites it into multiple targeted search queries and sends those to search engines. This process helps ChatGPT gather comprehensive information from multiple sources.

How do I find the keywords ChatGPT searches for?

To find ChatGPT's search keywords: start by listing all keywords in your niche, transform them into conversational user queries, predict the fan-out queries ChatGPT would generate, validate against Google SERPs, and cross-reference with actual ChatGPT citations. This reverse-engineering process reveals the queries you need to rank for.

How many search queries does ChatGPT generate per question?

ChatGPT typically generates between 3 and 5 fan-out queries for each user question that requires web search. Complex or multi-part questions may generate more. Each query targets a slightly different aspect of the user's question to ensure comprehensive coverage.

Does ChatGPT use Google or Bing for search?

ChatGPT primarily uses Bing as its search backend, though it may incorporate other sources. Different AI tools use different providers--Gemini uses Google, Claude uses Brave Search, and Perplexity has its own crawler. For maximum AI visibility, optimize for multiple search engines, not just Google.

How do I know if my content is being cited by ChatGPT?

Check ChatGPT's source citations at the bottom of its answers. Links from ChatGPT include "?utm_source=chatgpt.com" in the URL. You can also monitor your analytics for traffic with this UTM parameter to track when ChatGPT references your content.

What makes ChatGPT choose one source over another?

ChatGPT prioritizes sources that appear across multiple fan-out queries, have clear and well-structured content, include relevant keywords in titles and headers, and come from domains with established authority. Appearing in multiple search results for different variations of the same query increases your chances of being cited.

The Bottom Line

Finding the keywords ChatGPT searches for isn't guesswork. It's a systematic process of working backwards from user questions to fan-out queries to SERP analysis.

The methodology: build your keyword universe, transform keywords into user queries, predict fan-out queries, validate against SERPs, and cross-reference with ChatGPT citations.

The brands winning in AI search aren't the ones with the best content. They're the ones who've figured out exactly which queries ChatGPT is sending to search engines--and they've created content specifically targeting those queries.

Now you know how to do the same.