What Are ChatGPT Fan-Out Queries and How Do They Work in 2026?

What Are ChatGPT Fan-Out Queries and How Do They Work?

I was sitting at my desk last week running an experiment. I asked ChatGPT a simple question:

"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?"

ChatGPT gave me a list of 12 tools. Mailchimp, ActiveCampaign, HubSpot, and others. But here's what fascinated me - when I ran the exact same query in an incognito window, I got a different set of tools. Same question, different answers.

That sent me down a rabbit hole. I needed to understand what was actually happening behind the scenes when ChatGPT searches the web. What I discovered fundamentally changed how I think about getting brands visible in AI-powered search.

The answer lies in something called fan-out queries. (And if you want to learn how to reverse-engineer them, I've written a complete guide on how to identify the search queries ChatGPT sends to Google.)

What Is a Fan-Out Query?

A fan-out query is what ChatGPT creates when it needs to search the web for information.

Here's what most people get wrong: they think ChatGPT takes your question and sends it directly to Google. It doesn't. Instead, ChatGPT rewrites your question into multiple, more targeted search queries - typically 3 to 5 of them - and sends those to search engines.

OpenAI explained this in their 2026 blog post on how Search works:

"To provide relevant responses to your questions, ChatGPT search sometimes partners with other search providers. When it does, ChatGPT search typically rewrites your query into one or more targeted queries that it sends those providers."

They gave an example: if a biotech researcher asked ChatGPT, "what's the latest on the development of drugs that target CCR8 for cancer?" - ChatGPT might initially query a search partner using "CCR8 immunotherapy drug development 2025."

Notice what happened there. The conversational question got compressed into a keyword-rich search query. And critically, the year got appended. (This is why adding the year to your blog title significantly helps with ChatGPT rankings - ChatGPT expects fresh content to include the year.)

How ChatGPT Transforms Your Question Into Search Queries

Let me walk you through what happens when someone asks ChatGPT my email marketing question.

The original 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?"

ChatGPT extracts the key signals: Seed funded, B2B, SaaS startup, email marketing, tools.

Then it generates fan-out queries like these:

• 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

Each of these queries gets sent to search engines. ChatGPT then aggregates the results, reads through the top-ranking pages, and synthesizes an answer.

This is why the same question can yield different tool recommendations. Different fan-out queries might prioritize different search results, and ChatGPT might weight certain sources more heavily depending on how it interprets the user's intent.

Fan-Out Queries Have Tripled in Length

Here's something that caught my attention when analyzing ChatGPT's search behavior.

When researchers first studied ChatGPT fan-out queries, they found the average query length was about 5 words. Simple, direct searches like "best email marketing software."

From a recent analysis of over 600 queries, that average has jumped to 15 words per query. That's triple the length.

Look at some real examples from the dataset:

• "what features make medical accounting software and which are the top rated accounting systems for doctors and clinics software names and reviews"
• "top workplace management tools software for managing employees tasks and workplace operations 2025 reviews"
• "what is customer experience software, examples and how to evaluate customer experience platforms"

It wasn't uncommon to see ChatGPT search queries of 25+ words. These aren't simple keyword searches anymore. They're complete questions stuffed with context and qualifiers.

Why Does ChatGPT Add the Year to Search Queries?

Notice that ChatGPT often appends the current year to its fan-out queries. This isn't random.

When ChatGPT needs current information - pricing, tool recommendations, market comparisons - it signals recency to search engines by including the year. The biotech example showed "CCR8 immunotherapy drug development 2025." My email marketing queries generated "best email marketing tools for B2B SaaS startups 2026."

This has a direct implication for content creators: blog posts with the current year in the title are more likely to surface in ChatGPT's search results.

This is consistent with my client work. Blog posts with 2026 in the title consistently got picked up by LLMs compared to identical content without the year. If you're optimizing for AI search, make sure your content signals freshness.

Examples of User Queries vs Fan-Out Queries

Here's a breakdown of how different user questions get transformed into fan-out queries:

User Query	Example Fan-Out Queries
What CRM should I use for my startup?	best CRM software for startups 2026, affordable CRM tools for small businesses, CRM comparison for SaaS startups
I need help with project management for my remote team	best project management tools for remote teams 2026, project management software comparison, remote team collaboration tools reviews
Which email marketing tool has the best deliverability?	email marketing tools deliverability comparison 2026, best email deliverability rates by platform, email marketing software deliverability benchmarks

The pattern is clear: ChatGPT takes conversational questions and extracts the commercial intent, target audience, and key requirements, then reformulates them into search-engine-friendly queries.

Why Fan-Out Queries Matter for Your Brand's Visibility

If you want your product recommended by ChatGPT, you need to show up for the fan-out queries - not just the original user question.

Think about it: ChatGPT doesn't see your product directly. It sees whatever Google or Bing returns for queries like "best email marketing tools for B2B SaaS startups 2026." If your content doesn't rank for those specific queries, you're invisible to the AI.

To truly dominate, you need to understand how to identify exactly which search queries ChatGPT is sending - and then create content specifically optimized for those queries.

In my research, I found a direct correlation between how often a brand appeared across multiple fan-out queries and whether ChatGPT recommended that brand. Out of 12 email marketing tools ChatGPT suggested, 10 of them appeared in multiple search results across multiple fan-out queries.

Brands like Encharge and EmailToolTester showed up repeatedly across different fan-out queries. This repetition signals credibility to ChatGPT's algorithm - it suggests these sources know what they're talking about.

How to Identify Fan-Out Queries for Your Niche

Here's the process I use to reverse-engineer fan-out queries:

Step 1: List your primary and secondary keywords.

If you're an email marketing software, your keywords might include: email marketing, email marketing software, email marketing SaaS, B2B email marketing, transactional email, email campaigns, B2B email automation.

Step 2: Transform keywords into user queries.

These are the questions real users might ask ChatGPT. "What's the best email marketing tool for a SaaS startup?" "How do I set up email automation for B2B?" "Which email platform has the best deliverability?"

Step 3: Predict the fan-out queries.

For each user query, generate 3-5 search-engine-style queries that ChatGPT might create. Include the year. Add modifiers like "best," "comparison," "for startups," "affordable."

Step 4: Validate against actual SERPs.

Search your predicted fan-out queries in Google. Note which blogs and sources are ranking. These are your competitors for ChatGPT visibility.

How to Create Content That Ranks for Fan-Out Queries

Once you've identified the fan-out queries, create content specifically designed to rank for them.

Match your title tag to the fan-out query. If ChatGPT is searching "best email marketing tools for B2B SaaS startups 2026," your blog post title should hit those exact keywords. Not clever or creative - direct and keyword-rich.

Create multiple content pieces per user query. Remember, ChatGPT generates 3-5 fan-out queries for each user question. You want to appear in multiple search results. Create a main comparison post, a how-to guide, a detailed review, and maybe a Reddit post or LinkedIn article for additional coverage.

Include the year in your content. Titles, H2s, and meta descriptions should all signal recency. "Best Email Marketing Tools for B2B SaaS in 2026" will outperform the same article without the year.

Structure for LLM parsing. Use clear H1/H2/H3 headers. Include FAQs that mirror fan-out queries. Write in a direct, factual style - "X is..." answers that ChatGPT can easily extract and cite.

Frequently Asked Questions About ChatGPT Fan-Out Queries

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 - typically 3-5 - and sends those to search engines like Google or Bing.

How many fan-out queries does ChatGPT generate?

ChatGPT typically generates between 3 and 5 fan-out queries for each user question that requires web search. For complex or multi-part questions, it may generate more. The exact number depends on the complexity of the query and the type of information needed.

Does ChatGPT use Google to search the web?

ChatGPT primarily uses Bing as its search backend, though there's growing speculation it may incorporate Google results as well. Different AI tools use different search providers - Gemini uses Google, Claude uses Brave Search, and Perplexity has its own crawler. This means optimizing for AI search requires visibility across multiple search engines.

Why does ChatGPT give different answers for the same question?

ChatGPT can give different answers because: the fan-out queries it generates may vary slightly each time, search results change constantly, and the model may weight sources differently based on context. Personalization also plays a role - logged-in users may see different results than incognito sessions.

How do I optimize my content for ChatGPT fan-out queries?

To optimize for fan-out queries: identify the search queries ChatGPT is likely to generate for your niche, create content with title tags that match those queries exactly, include the current year in titles and headers, structure content with clear H1/H2/H3 headers and FAQs, and ensure your content ranks in traditional search engines first.

The Bottom Line on Fan-Out Queries

ChatGPT doesn't search the web the way humans do. It transforms conversational questions into multiple targeted search queries, aggregates the results, and synthesizes answers from what it finds.

For brands trying to get recommended by AI, this changes everything. You're not optimizing for user questions directly - you're optimizing for the fan-out queries ChatGPT generates behind the scenes.

The playbook: identify your target fan-out queries, create content that ranks for each one, include the year to signal freshness, and build presence across multiple queries to establish credibility.

The brands that show up repeatedly across fan-out queries are the brands ChatGPT recommends. It's that simple - and that hard.