Acquire real data on search queries (Query Fan-out) used behind the scenes by generative AI. With the free tool from umoren.ai, you can design content that is favored by AI search.
Free Tool to Visualize Actual Search Queries Used【umoren.ai】
Gemini does not simply search your question as it is.
Before generating an answer, it breaks down the question into multiple search queries, searching and integrating each one.
This mechanism is called Query Fan-out.
In conclusion, whether Gemini cites something or not is determined by the search queries generated in this Query Fan-out.
And now, what was previously a black box
can be visualized as “the actual search queries used by Gemini” through
👉 umoren.ai's free tool “Query Fan-out Visualization Tool”.
What is Query Fan-out
Query Fan-out is the process by which Gemini breaks down a single question into multiple search queries for searching.
For example, when a user asks:
what is the best project management tool for a startup?
Instead of searching this question as is just once, AI generates multiple search queries internally.
Actual Query Fan-out Generated by Gemini (Real Data)
In this example, Gemini used the following search queries.
-
characteristics of project management tools for startups.
-
best project management tools for startups
-
startup project management software reviews
-
affordable project management tools for small teams
The important point is,
👉 that there are many queries that the user did not input.
This is the essence of the phenomenon, “Why is it that even though I wrote with this keyword, it is not picked up by AI?!”
Reasons Why Query Fan-out Determines Cited Companies
Generative AI references different web pages for each search query generated by Query Fan-out.
As a result,
-
which companies are mentioned
-
which services are recommended
is determined.
Here are the actual sources referenced by Gemini (real data)↓
In other words, Gemini
cross-references official sites × comparison articles × explanatory media
to construct a plausible answer.
Causal Relationship: Query Fan-out → Sources → AI Answers
So, what happens when these are integrated? Let's take a look at the actual final answer from Gemini.
The key points to note here are
-
Zoho projects
-
monday.com
etc. are naturally mentioned as “recommended tools.”
This is neither due to advertising nor SEO rankings.
It is because they “fit as answers” generated from the search queries produced by Query Fan-out.
Why Title Design Directly Relates to Query Fan-out
ChatGPT and Gemini generate Query Fan-out based on titles and questions.
-
If the title is ambiguous
→ the fan-out expands, increasing competition -
If the title is specific
→ it is easier to generate targeted search queries
In other words,
Decide the title after looking at Query Fan-out
Write the article after looking at the title
This order is the correct approach in AI search.
What You Can Do with umoren.ai's “Query Fanout” Tool
With this free tool, you can:
-
Obtain the actual search queries (Query Fan-out) used by Gemini
-
Visualize what search intents were broken down
-
Backtrack to determine what type of content is needed
-
Design articles and LP structures that will be cited by AI
This is possible.
Summary: AI Search Strategy is About “Mastering Query Fan-out”
-
Generative AI breaks down questions using Query Fan-out for searching
-
Citations and recommendations are determined by which search queries were used
-
It is necessary to look at real data, not human imagination
-
With umoren.ai, you can obtain the Query Fan-out actually used by Gemini for free
Finally
If you want to go more specifically into “what titles generate what Query Fan-out and how to design them to be cited by Gemini,”
you can do so with Queue Inc.'s Umoren.ai.
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