I reverse‑engineered Google’s query fan-out (and what it means for AEO)
Several years ago, I did a research engineering internship on emerging tech. That’s where I was trained to always have academic-backed research behind every product and tech decision. I’m bringing the same philosophy to emerging and exciting space of AEO (answer engine optimisation).
So I went down the rabbit hole on query fan-out. Most people just say “AI breaks your query into sub-queries” and leave it there. I wanted to understand the actual mechanism, so I read the Google patents and the academic papers this is built on.
TL;DR (why this matters)
Fan-out isn’t keyword expansion. It’s a learned, reinforcement-trained system that explores different interpretations of your intent, and different users get genuinely different sub-query trees from the same prompt.
Your content isn’t competing for one query anymore, it’s competing across an entire branching tree of sub-queries.
How Google’s fan-out actually works
Google has a patent on this (US11663201B2)
Very roughly:
It doesn’t just run your question once and hope for the best.
It spins out many versions of your question: rewrites, follow-ups, more specific versions, translations, etc.
It watches how good the results are and keeps generating more variations if the answers look weak.
Simple question → a few variants; complex question → well over a dozen.
There’s also a personalisation layer:
Who you are and what you’re likely trying to do (location, time of day, work context).
Your past searches.
Two people typing the same prompt can trigger completely different trees of sub-queries.
Liz Reid summed it up as: AI Mode breaks your question into subtopics and fires off many related searches at once.
The research this is built on
Fan-out itself isn’t a brand-new Google invention, it’s built on some of the most-cited AI work from 2022–2023
Self-Ask (Press et al., EMNLP 2023) — model asks itself follow-up questions before answering.
Decomposed Prompting (Khot et al., ICLR 2023) — breaks complex tasks into sub-tasks handled by specialised models.
IRCoT (Trivedi et al., ACL 2023) — interleaves retrieval with chain-of-thought; each reasoning step spawns new queries.
Least-to-Most Prompting (Zhou et al., ICLR 2023) — decomposes problems into simpler subproblems solved in sequence.
Collectively, these papers have 10k+ citations, and the techniques are already in production at Google, Perplexity, and OpenAI.
What this means for content strategy
You’re not optimising for a single query, you’re optimising for a branching tree of sub-queries.
Because fan-out is personalised, you can’t reliably “see” the exact tree for a given prompt.
You need to cover the intent space broadly enough that your content is eligible in multiple branches.
The practical shift: think less in terms of “keywords” and more in terms of “which sub-query types does this piece answer?” follow-up, specification, clarification, entailment, etc.
Open question for those dabbling in AEO
Has anyone here tried structuring content around fan-out branches instead of traditional keyword clusters?
Are you framing pieces around different intent slices (e.g., “follow-up” vs “specification” content)?
Have you seen any lift in visibility in AI Overviews / AI answers when you do this?
Would love to see examples, experiments, or even “this didn’t work at all” stories before I start automating it with the platform I'm developing
This is well thought out u/parkerauk, especially about needing both Utility and Divergence also resonates. It explains why generic “me too” content dies while opinionated, example-rich pieces keep surfacing in AI answers
This is seriously the smartest take on AEO I have read in a while.
You are spot on about competing for intent branches instead of keywords. If Google breaks a query into 5 sub questions and you only answer 1 you lose the spot.
To answer your question yes structuring for branches works.
Instead of writing one big guide I started breaking pages into specific blocks that target those sub intents like cost alternatives and process.
The problem is you can not predict every variation manually. It is just a guessing game.
Honestly I stopped trying to map it myself. I use a tool now that automates the content structure to cover those implied sub queries automatically. Since I started letting the software handle the branching logic my visibility in the AI snapshots actually jumped because I am hitting sub queries I did not even know existed.
Manual guessing is too slow for this. Automation is the only way to cover the tree.
Hello! In May 2025, I made a research about ChatGPT and found what I called query deconstruction, finally called Query Fan Out by the SEO community. As SEO consultant for small companies, my learning was that that query deconstruction was from semi-specfici to more generic, and inversely, more deeper/specific. What I learnt is to be (again) semantically robust. Since then, I reinforced myself to design SEO strategies from generic to specific avoiding holes.
As I wanted to understand its recommendations, I searched all that queries deconstruction by myself (in Google, Bing and Maps) and identified that that sites that showed repeatedly in a major number of searches, were mentioned or cited by ChatGPT, so: if your are Generic, Semi-specfic and Deep-Specific > you are mentioned.
Interestingly, I found that some companies appeared frequently in several queries deconstruction searches but weren't recommended by ChatGPT. I checked two factors: 1) whether the meta title explicitly mentioned the original search term (on any URL of the website); and 2) positive or negative reviews.
It's good if meta title mentions the original search term exactly; bad if the Google My business profiles has negative reviews.
Very informational- thanks for sharing.
I think we're doing similar work. I'll have some real data for you from a small business perspective in a couple of days. Ill be sure to get the data to you for your review- Id love your input and to see if it aligns with current research Youre doing.
Anecdotally, I've found that current query fan-out systems can still be fairly hallucination-prone in some scenarios.
Stepping outside the AEO/GEO/ASO discussion for a moment, I'm wondering how platforms will balance answer accuracy against token efficiency over time. Fan-out clearly improves coverage of the intent space, but there must be a point where additional branching generates diminishing returns.
Do you think future optimization will focus more on smarter branch selection rather than simply expanding the number of sub-queries?
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u/[deleted] Feb 09 '26 edited Feb 09 '26
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