Most content teams are optimizing for the exact queries users type. Most content teams are missing the mark.
When a user submits a prompt, ChatGPT performs a query fanout – multiple additional searches that expand the original query.
Content that consistently appears across fanouts is more likely to be cited than content optimized explicitly for the original query.
“Best” is the most frequently injected word in ChatGPT’s query fanouts, explaining why “best of/for” roundups consistently dominate AI-generated answers.
ChatGPT actively searches for reviews, even unprompted – meaning that third-party review presence directly shapes AI-generated brand descriptions.
Multi-angular content strategy is becoming imperative for AI visibility.
Executive Summary: A Peec AI analysis of 5 million hidden AI searches reveals that ChatGPT expands user queries into multiple parallel sub-searches before synthesizing any response – the process known as “query fanout”. The algorithm rewards breadth of coverage over depth or keyword precision, with third-party reviews and content freshness being major trust signals. For brands not yet optimizing for multi-angle AI visibility, this is an immediate call to action.
Here’s a scenario that likely plays out a thousand times a day: a potential customer types, for example, “What’s a good CRM for a remote team?” into ChatGPT. However, the model doesn’t just return “HubSpot is a good CRM for a remote team.”
Instead, the user gets several recommendations based on criteria such as team size, technical maturity, and budget, each with pros and cons, and why it works for the specific use case – a comprehensive breakdown that answers the user’s question and then some. The twist?
ChatGPT did NOT search for “What’s a good CRM for a remote team?”
Instead, it performed a series of related searches in the background and synthesized THOSE results in a single, comprehensive answer. The initial question was a part of the equation – but the exact-match page isn’t. This is the new competitive reality of AI-driven SEO.
And most content strategies aren’t built for it.
Not the query. Well, not just the query. When a user submits a question, ChatGPT (and other AI search engines) perform something called a “query fanout” – parallel searches covering multiple facets of the initial question, such as brand names, comparison articles, review summaries, current-year roundups, and specific use-case variants.
With this mechanic employed, the introductory question “What’s a good CRM for a remote team?” gets expanded into several complementary queries*, such as:
*NOTE: ChatGPT does not expose its full internal chain of thought or full reasoning process; the above illustrative examples are based on documented injection patterns – actual fanouts may vary.
Once the model gathers the results from all sub-queries, it synthesizes them into a single, complete response. Now, according to Peec AI’s recent analysis of 5 million fanouts, ChatGPT generates an average of 2.1 expansions per prompt, each of which returns many potential citation sources, bringing us to the next question.
![A screenshot displaying ChatGPT query fanout in action (Green: original query intent modifier; Red: Intent modifier injections; Blue: Fanout criteria) [Image credit: ZeroClick Labs via ChatGPT]](https://zeroclicklabs.ai/wp-content/uploads/2026/06/Screenshot-2026-06-05-103036.png)
At the core of ChatGPT’s deciding algorithm is Reciprocal Rank Fusion (RRF). As Metehan Yesilyurt, a researcher who identified the algorithm inside the ChatGPT source code, put it – RRF is “a method to combine search results from multiple queries into one final ranking.”
How RRF works is, in simple terms, instead of asking “which page ranks #1 for the original query?” it asks “which page appears consistently across the most fanouts?” This means that a page ranking #4 across ten fanouts will outperform the one ranking #1 for two, effectively flipping the winning criteria for AI search upside-down.
Content appearing across the most query fanouts wins the citation. The rest remains invisible.
You may have noticed that, in our fanout examples, certain words were added to the queries even though they weren’t a part of the original question: best, 2026, vs, reviews, top, features, and comparison.
This is not a coincidence. ChatGPT injects these intent modifiers into fanout queries systematically – to surface content formats most useful for synthesizing a comprehensive, up-to-date recommendation: rankings, comparisons, and reviews.
The importance of this finding cannot be overstated, as it finally explains why listicles, “X vs Y” matchups, and “best of/for” roundups dominate AI-generated answers. It’s something content and SEO teams have suspected for years – but Peec AI data has now made it explicit.
Directly and deliberately. ChatGPT’s fanout behavior points to four content strategy shifts: use-case-specific framing consistently outperforms generic product/service description, covering multiple angles matters more than going in-depth on one, third-party review presence actively shapes AI-generated brand recommendations – managed or not, and content freshness is a major trust signal. In practice, this means:
Framing your offer as the best solution for a defined audience or a specific scenario is what earns fanout coverage – and, consequently, citations.
Why: ChatGPT routinely rewrites queries into “Best of/for” searches. Content framed around a specific use case naturally aligns with the sub-queries.
Create content clusters that showcase what the product does, who it’s best for, how it compares to other solutions, pricing tiers, and what existing users say.
Why: RRF rewards consistent presence across multiple sub-queries – a content cluster covering the complete decision journey will consistently outperform a single highly-optimized page. In addition, the search behavior is changing – users no longer arrive with one, cleanly defined question, but they still expect a comprehensive answer. AI search is structurally built to match that layered complexity.
Treat third-party presence as an AI visibility signal: audit which sites ChatGPT is actually pulling from, and manage reviews accordingly.
Why: ChatGPT treats reviews as trust signals, and searches for them even without being prompted. Plus, it can go beyond just the biggest sites, meaning that an outlier rating on an obscure platform can reshape how ChatGPT describes your brand at scale.
Rather than refreshing your entire content library, prioritize updating pages that ChatGPT already surfaces as sources.
Why: Both Peec AI’s data and broader AI citation research confirm recency as a top-tier signal for source selection. A freshness update to pages AI already cites has the potential to deliver immediate, measurable lift.
Tina Clarke is the AI SEO Manager at ZeroClick Labs, specializing in AI search optimization and Generative Engine Optimization (GEO). With a strong foundation in content strategy, technical SEO, and operations, she leverages her expertise to help brands shift from traditional rankings to discoverability and excel in AI-driven ecosystems.
Act now and reap the early-mover advantage
ChatGPT isn’t searching for your keywords. It’s searching for angles your content doesn’t cover.
ZeroClick Labs can audit exactly where your content drops out of ChatGPT’s fanout process – and build the coverage to put you right back in.
With Google Search pivoting toward an agentic model and AI traffic becoming a prime acquisition channel, presence in synthetic answers is more critical than ever.
“Our agency had no idea how to approach AI visibility. ZeroClick only does this one thing so they actually know what works. Worth every penny just to not waste time figuring it out ourselves.” – Jay