ChatGPT Query Fanouts Reveal AI Search Input Dynamics

A study of five million query fanouts collected between April 1 and April 21, 2026, by Peec AI, has revealed how artificial intelligence platforms like ChatGPT modify and expand user queries before executing searches, significantly influencing how brands appear in AI-generated answers. The analysis indicates that ChatGPT consistently inserts terms such as "best," "reviews," and the current year into user prompts, even when these keywords are absent from the original request. This practice suggests a deliberate strategy to refine search parameters for potentially more relevant or commercially oriented results. For instance, the frequency of ChatGPT query fanouts that include the term "reddit" saw a substantial increase, growing from approximately 0.15 percent in January 2026 to 3.68 percent by May 2026, highlighting a growing reliance on or integration of social media discussions in AI responses. The study also detailed that ChatGPT employs Reciprocal Rank Fusion (RRF) as a method for scoring search results. Under RRF, content that appears across multiple fanout searches receives a higher score than content that is only surfaced by a single sub-query. This mechanism incentivizes content that is broadly relevant to various facets of an initial user prompt. The findings from Peec AI suggest that marketers aiming to optimize for visibility in AI-driven search results should shift their focus from tracking citations, which represent the output of AI searches, to understanding fanouts, which represent the input that determines the possibility of a citation. Fanout analysis, according to the study, should become a standard component of AI-driven SEO (AEO) audits, on par with citation tracking. When a user poses a question to ChatGPT, the AI does not perform a direct search for that exact phrase. Instead, it generates and executes a series of related sub-queries concurrently, each exploring a different dimension of the original prompt. The results from these sub-queries are then merged to construct the final answer. For example, a query such as "best project management tools for remote teams" might trigger simultaneous fanouts for "top project management software 2026," "remote team collaboration features," "project management pricing comparison," and "enterprise versus small team project management tools." The comprehensive answer provided by ChatGPT is derived from the aggregation of information from all these parallel searches, rather than solely from content that precisely matches the initial wording. This underlying process of query expansion and multi-faceted searching is crucial for understanding how AI platforms interpret and fulfill user requests, and consequently, how content needs to be structured and optimized to be discoverable within these evolving search ecosystems. The study's examination of five million query fanouts from ChatGPT, Perplexity, and Grok provides a behind-the-scenes look at the operational mechanics of AI search platforms and their implications for brand presence.