Google’s Query Fan-Out Technique and What SEOs Should Know About It

A lot has been written about Google’s query fan-out technique already. This is my attempt to explain it.

Query fan-out is used by Google’s AI Mode and AI Overviews. It was announced with the introduction of AI Mode around March of 2025.

How query fan-out works is that when the user searches a query or prompt in one of Google’s AI search surfaces, Google doesn’t just search for that term alone. It also searches for a series of sub-queries (synthetically generated).

The idea behind query fan-out is for Google to provide an AI-generated answer with a richer context that satisfies the user’s journey by encompassing multiple relevant points of information.

For example, if I search for “does washing your hair less make it fall out,” Google’s AI Mode may search for related sub-queries like:

• does product buildup on scalp cause hair fall
• hair washing frequency for oily scalp and hair loss
• how much hair is normal to lose daily

And maybe dozens more.

Here is how Google described the query fan-out technique in its May 2025 blog post unveiling AI Mode:

“Under the hood, AI Mode uses our query fan-out technique, breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf. This enables Search to dive deeper into the web than a traditional search on Google, helping you discover even more of what the web has to offer and find incredible, hyper-relevant content that matches your question.”

– AI in Search: Going beyond information to intelligence, Elizabeth Reid

If potentially dozens of fan-out queries wasn’t enough, Google is also rolling out Deep Search, which uses the fan-out technique to look at potentially hundreds of sub-queries:

“Deep Search uses the same query fan-out technique but taken to the next level. It can issue hundreds of searches, reason across disparate pieces of information, and create an expert-level fully-cited report in just minutes, saving you hours of research.”

How can you simulate Google’s query fan-out technique?

Google is using LLMs (a custom version of Gemini 2.5) to generate its synthetic queries when it does the fan-out technique.

Some enterprising SEOs have built tools that mimic this behavior.

My favorite (because it’s free and easy) is the “AI Visibility & Coverage Analysis” tool from Locomotive Agency. Here’s an example of its output for that “does washing your hair less make it fall out” query:

Other cool fan-out tools include “Qforia” by iPullRank (requires a Gemini API) and “Google AI Search: Simulating Query Fan-Out Visibility” by WordLift.

Why is knowing about the query fan-out technique beneficial to SEOs?

The obvious answer is because it’s important to know how search works, and query fan-out is a departure from traditional search.

Whereas traditional search used to look at single queries (although likely expanded already to a degree) and assess the relevance of entire documents for them, Google’s AI search surfaces like AI Mode and AI Overviews look at potentially dozens of queries and assess document relevance at a passage level.

Traditional content production used to start with a single query and focus on its intent, that was the point of keyword research.

But knowing the potential fan-out queries for a search allows you to incorporate that information as relevant passages into your document, or to create supporting documents for those sub-queries to build topical authority.

How does the query fan-out technique affect rankings?

It’s a good question.

Before we knew about the query fan-out technique, people were analyzing AI Overviews and finding citations from outside of the top 10 links for a query. Is it possible you can appear in an AIO even with poor rankings?

Well, we later realized that those citations likely were there because they ranked well for fan-out queries.

It’s the same with AI Mode.

Now, these AI search surfaces are probabilistic and personalized, which means two people searching the same query might see dramatically different results.

Having said that, the more relevant you can make the passages in your documents (or other media) to fan-out queries, the likelier you’ll be to earn a mention or a citation in the AI-generated answer.

So for the question of how query fan-out impacts rankings, just know that we’re no longer optimizing for individual keywords but rather entire user journeys, and those fan-out queries guide the way.

Where else can I learn about Google’s query fan-out technique?

Here are some resources I’ve read and would recommend:

• Google AI Mode’s Query Fan-Out Technique: What is it and How Does it Mean for SEO? – Aleyda Solis
• Query Fan-Out: A Data-Driven Approach to AI Search Visibility – Andrea Volpini
• Understanding Query Fan-Out in Google’s AI Mode – Marie Haynes
• How AI Mode Works and How SEO Can Prepare for the Future of Search – Mike King

What’s next?

I’d recommend checking out the articles above as well as playing with some of the query fan-out tools I linked to above.

If you’re looking for actionable takeaways, they’re quite simple:

• Analyze fan-out queries to better understand your audience’s user journey.
• Create content that answers those queries, with special attention paid to dedicated passages that are semantically relevant.

I’m not a big fan of optimizing for machines, even given the rise of agentic search. Personally, I’ll always advise site owners to write for people first.

Having said that, we’d be naive to ignore the query fan-out technique, as it changes the equation for what it means to do keyword research and content production.

I’ll return to this article to add more information as I get it.

Until next time, enjoy the vibes:

Thanks for reading. Happy optimizing! 🤗

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