The way AI models generate responses is fundamentally reshaping the landscape of content strategy. Modern systems like ChatGPT, Perplexity AI, and Grok often don't rely solely on a user's initial prompt. Instead, they kick off a process known as the query fan-out, which has major implications for visibility and authority.
To optimize content for LLMs, we must look beyond traditional single-keyword ranking and embrace a strategy focused on broad topical clusters.
Decoding the Query Fan-Out
A query fan-out refers to the related keywords that an AI generates internally in response to a user's prompt. These LLMs use these additional keywords to broaden the base of their research, ensuring the final answer is comprehensive and well-grounded.
The search results for these fan-out queries directly influence the sources and facts that the AI ultimately includes or cites in its final summary. Therefore, if content creators want their work to be visible to AI models, they must ensure their pages rank well for this entire expanded set of search terms.
The Instability Problem: Why Traditional Keyword Hunting Fails
While identifying fan-outs seems like a direct path to content optimization, a critical challenge arises: Large Language Models (LLMs) are non-deterministic. This means that if you give an AI the exact same prompt multiple times, the set of fan-out queries it generates can change dramatically from one run to the next.
This volatility is significant. Research indicates that only about 27% of fan-out keywords remain consistent across different searches. Furthermore, a substantial 66% of fan-out keywords appeared only once in the data observed. Attempting to target and optimize for individual, "wobbling" fan-out queries is inefficient and unlikely to yield reliable long-term results.
The Semantic Closeness: Why Clustering Works
Despite this instability, fan-out queries are not random. They are highly semantically similar to the original user input. Measured by cosine similarity, most fan-out queries are located between 0.75 and 0.95 of semantic alignment.
This high degree of closeness demonstrates that the AI is not wildly jumping to unrelated subjects; rather, it is logically expanding on the user’s topic. The AI is looking for related contextual information to build a comprehensive answer, which validates the use of content clusters.
The Solution: Content Clustering
Since chasing individual, inconsistent fan-outs is a losing battle, the most effective strategy is cluster-level optimization.
Clustering mitigates the non-deterministic nature of the AI by focusing on the broader themes that consistently emerge. Data shows that 90% of fan-outs can be effectively grouped into up to four stable clusters around the initial query.
To successfully adapt to this AI-driven search environment, content creators need to:
- Target Wider Topics: Move away from optimizing for highly specific single keywords and start targeting wider keyword groups and big-picture themes.
- Build Comprehensive Content Hubs: Content must demonstrate expertise across a broad range of related topics. By creating comprehensive content hubs that cover all aspects of a topic, you build a stronger base that is less susceptible to disruption by individual query changes.
- Understand Intent: Thoroughly understand the full scope of questions and intent your audience has, and answer these questions in a comprehensive way using verbiage that is easy for the AI to cite or summarize.
By focusing on these durable topical clusters, content creators can increase the probability that their content will be used by models like ChatGPT, Perplexity AI, and others, thus ensuring visibility in the evolving era of AI search.
The approach to content visibility in AI is like creating a strong river delta instead of a single stream. The single stream (the fan-out) might change its course slightly with every passing storm (every prompt), but by building comprehensive content across the entire delta (the cluster), you guarantee that your resources flow into the main current, regardless of the exact path the AI chooses.
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