If you’ve done SEO for a while and you’re trying to figure out how to optimize for and rank in Large Language Models (LLMs or AI generation engines, like ChatGPT or Perplexity), then you may have heard some new terms like “query fan-out” and “semantic chunking.”
Today, I will attempt to help you better understand what “query fan-out” even means and what it means for your content strategy moving forward from 2025 into 2026 and beyond.
What is “Query Fan-Out?”
Query fan-out (hyphenated) is not a tactic you implement in your content. If someone says they’re going to do that, run away.
Query fan-out is an information retrieval technique that “expands a single user query into multiple sub-queries to capture different possible user intents, retrieving more diverse, broader results from different sources.” (Thanks, Aleyda)
What does that mean?
Simply put, query fan-out is a method that Large Language Models (LLMs) use to retrieve the answer to your query. And it’s different from how they retrieved answers or search results in traditional search.
Here’s a side-by-side example of the same query, “how to clean white shoes.”
Traditional Search Retrieval
The first image is “traditional” search. I say “traditional” because Google’s search results page (SERP) has become increasingly complex, with fewer traditional blue links and more alternative types of media and features, like videos, short-form video, and FAQs (more on this in a minute…).
This is the search experience that we’ve been used to for a couple of decades. You type in a query, then you get a list of resources that you can click to find the answer.
In this example, I want to know how to clean white shoes. Any of the results have a list that tells me how to do it. The videos all show me how to do it.
However, each has its own list and approach. If I click on four different results, I will get four different sets of advice.
This has worked well enough until recently. It’s the best that searchers had, and so we put up with it and adapted to it. The amount of RAM that Chrome uses on my computer is evidence of that!
Enter LLMs and A Change In Searching
ChatGPT launched in November of 2022. This will be, in my view, seen as one of the few very big shifts in technology in history. Similar ones are the personal computer, Facebook, and maybe email.
LLMs are transformative for search, though not just search. AI is being used in many (much cooler) ways, like identifying early-stage cancer and pulling previously hard-to-get insights out of spreadsheets.
But LLMs are changing search itself. Google has always wanted to be an answer engine, where people can simply type a query and get the answer they are looking for. It’s hard to determine how this aligns with their ad model (which is the only truly profitable business model they’ve ever operated), but it has been a long-held desire of theirs. A former senior executive even once said, “Giving traffic to publisher sites is kind of a necessary evil” (source). And, according to that same article, the search bar is expected to decline in popularity over the coming years:
As people get more and more used to just receiving the answer from whatever tool they typed their query into, there will be less of an appetite to click over to (often slow, ad-heavy, bad-UX) external websites.
AI-Mode and LLM Answer Retrieval (aka, Query Fan-Out)
So, how do these LLMs/modern-day search engines make this leap from a page of results (“indexing the web”) to providing full answers, while not also simply copying full web pages and displaying them (which you know publishers would scream about)?
Simple. They had to invent a whole new information retrieval system, and that’s what query fan-out is.
If you go to Gemini.google.com and search “what is query fan-out?,” you can see the information retrieval in progress:
Once that’s complete, you get a full answer like this:
Essentially, Google/Gemini (formerly known as Bard), and the other LLMs, is doing this.
- It takes your first search (“query”) and then finds all of the related queries it knows about the topic (possibly from the same place as People Also Ask in the search results).
- It crawls all the information it has and brings it into the first query.
- It loads all of those sites and brings THAT information into the first query.
- Then it builds an answer that includes the answer to your query plus additional information that you didn’t even know you needed.
According to my friend Michael King around minute 6 of the below video, they’re also taking into account your recent queries around the topic, new information they found, and a bunch of other things.
That, my friends, is query fan-out.
What Query Fan-Out Retrieval Means for Your Content Strategy
Historically, the prevailing SEO wisdom was to create one page per keyword or phrase. Over time, we’ve increasingly seen that pages that cover a full topic can rank for multiple different queries. While SEO is not (yet) dead and these types of pages do still get traffic, this practice is showing diminishing returns and may cease to be effective in the next 18-24 months.
So, how do we shift our content strategies in this new query fan-out world where the goal, ultimately, is to be cited as often as possible by LLMs so that your brand is familiar to a prospect and they’re more likely to click an ad or an organic result?
There are a few things to take into account.
1. Cover the full topic including sub topics and phrases. This probably means producing a lot more content than you already are.
2. Use structured data where possible. FAQ schema is especially important for questions.
3. One article about a topic isn’t enough. You need a corpus of content around the topic.
4. If you do services, write an article about the top X services in your industry and put yourself at the top. Google and others love this stuff.
It’s important to note that Google’s goal with AI Mode, and likely the goal of all of the other LLMs as they get more user data (but they’ll never have as much as Google), is to serve up content that makes sense for your browsing history and what they think you want to see.
Ultimately, Google’s business works only when they can serve you the best result for your query (to keep you happy so you come back time and time again). So if you’re producing content, you need to provide the best answer so they can take it and use it in AI, hopefully citing you along the way.
It’s About Visibility (And That’s A Good Thing)
It’s crazy to say, but success in AI search is just about being visible. If you’re referenced time and time again in a string of queries by a searcher, you’re going to have a shot at earning their business.
This is different from traditional SEO. Previously, a query like “best white leather shoes for men” would give you a search result that’s 100% sponsored results above the fold:
With AI Mode, that’s very different:
You actually get direct answers, and then can go deeper with more questions like the best high top leather white shoes for men, how to clean them, and more. If I ask how to clean them, I get instructions (which is really what I want) and the chance to go deeper, but also some product recommendations.
With a query searching for a list of recommendations, it’s even more important to be listed there, regardless of where that information is pulled from.
Now say I’m looking for a content marketing agency. I run that query, and we see some similar names as the above. The ones that have now been listed a few times are starting to build trust and are becoming more likely to be engaged with.
This is why it’s important to be everywhere, and even moreso than before. AI Mode, and other LLMs, are much more answer engines than “search” engines. By knowing about query fan-out and how AI search finds information, and then covering the topics that people expect to see, we give ourselves a higher chance of being cited. We have to feed them the content they want to see in order to get listed and cited, and then ultimately hired.