The proliferation of Large Language Models (LLMs) like ChatGPT, Perplexity AI, and Grok has introduced a fascinating, yet challenging, layer to content optimization: the query fan-out. This is the internal research process where the AI generates multiple related search terms to ensure its final answer is comprehensive and well-grounded.
For content creators aiming for visibility in this new landscape, understanding the mechanics of the fan-out—especially its volatility—is crucial.
The Data Driving AI Search Volatility
While analyzing the specific keywords an AI generates seems like the ultimate optimization secret, the reality of non-deterministic LLMs complicates this approach. When the same prompt is entered twice, the specific fan-out queries produced often change dramatically.
Research highlights the critical data points that prove the instability of single fan-out queries:
- Low Consistency: Only about 27% of fan-out keywords remain consistent across different search runs. These few terms are the "core keywords" that consistently influence the topic.
- High Fragmentation: A staggering 66% of fan-out queries appeared only once in research data.
This means that focusing your content production on individual fan-out terms is highly inefficient. You would be chasing keywords that likely disappear after one search, leading to fragmented content and wasted effort.
The Semantic Closeness of Query Neighbors
Fortunately, the volatility is constrained by semantic relevance. Even though the specific wording of a fan-out changes, the core topic remains highly related to the original input. Fan-out queries are highly semantically similar to the initial query, typically aligning between 0.75 and 0.95 of cosine similarity.
This indicates that the AI is not randomly surfing the web but is logically expanding on the user’s topic to build semantic depth.
This phenomenon gives rise to the concept of query neighbors. These are fan-out keywords that, despite their different wording, share common search results (URLs) with the original query. Data shows that 84% of fan-outs are "query neighbors" that share at least one URL with the original query's top search results. These queries are inherently linked and should be treated as a single thematic unit.
The Cluster Strategy: Stability Through Aggregation
The key to succeeding in an environment driven by unstable fan-outs is to move beyond single-keyword optimization and focus on cluster-level optimization.
By aggregating the data from multiple fan-out logs, you can identify the stable, broader themes that consistently emerge. Clustering mitigates the 66% instability problem by focusing on these durable themes. This strategy is supported by the finding that 90% of fan-outs can be split into up to four stable clusters around the original query.
Content creators must, therefore, evolve their focus to:
- Target Wider Topics: Aim for comprehensive content that satisfies the entire spectrum of related search terms represented by the cluster, rather than just the initial keyword.
- Build Authority Hubs: Create content that demonstrates expertise across all aspects of a topic, ensuring that your content ranks for the entire cluster of query neighbors. This holistic approach makes your content a more robust and reliable source for the LLM to pull from.
By optimizing for these durable topical clusters, content producers ensure that their content is visible regardless of which slightly different fan-out query the AI model (be it ChatGPT, Perplexity AI, or Grok) uses in a particular search instance. This shift from chasing volatile keywords to mastering semantic depth is essential for establishing authority in the age of agentic AI search.
Optimizing for content clusters is like tending a vineyard instead of chasing individual grapes. The wind may blow some grapes (fan-outs) one way or another, but if the entire vine (the cluster) is healthy and robust, the harvest (visibility and authority) remains consistent and strong.
Shift from Keywords to Clusters.
Ready to build durable authority for the LLM era?