Query Fan Out - See What AI Searches Internally | LLM Pulse

Query Intelligence

See what queries AI searches internally when answering prompts

When ChatGPT answers a question, it performs web searches, consults tools, and researches sources before generating text. Query Fan Out extracts these hidden queries directly from the API, showing you exactly what AI considers relevant to your tracked prompts.

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Features

See Inside AI's Query Logic

AI models internally search, research, and cross-reference before answering. Query Fan Out exposes this hidden layer, showing you exactly what queries ChatGPT considers relevant to your tracked prompts.

Extract AI's Own Related Queries

When ChatGPT answers a prompt, it internally generates related web searches and tool queries. We capture these exact queries, showing you what the AI considers relevant.

See AI's Web Searches

AI models like ChatGPT perform web searches to answer questions. We extract these search queries directly from the API response, revealing what sources AI is consulting.

Understand AI Topic Mapping

Discover how AI models mentally connect topics. The fan out queries show you the semantic web of related concepts AI uses to contextualize your industry.

Uncover Hidden Query Patterns

Beyond your tracked prompts, see the sub-queries and search refinements AI uses internally. These reveal search intent you might never have guessed.

Identify Tracking Gaps

Fan out queries often reveal important related topics you're not monitoring. Add them to your tracking to ensure complete visibility coverage.

Extracted From Live AI Responses

Query fan out data comes directly from live ChatGPT API responses, not predictions or guesses, but the actual queries AI used to formulate its answer.

How Query Fan Out Works

Extract the hidden queries AI uses when answering your prompts

  1. Execute Prompts

When your tracked prompts run against ChatGPT, the API returns rich structured data beyond just the text response.

  1. Extract Fan Out Queries

We parse the API response to capture web_search_results, search_results, tools, and sources, each containing related queries the AI used.

  1. Analyze Query Patterns

See which related queries appear most frequently, how topics cluster, and what search patterns AI uses for your industry.

  1. Expand Your Tracking

Add relevant fan out queries to your prompt tracking. Build comprehensive coverage based on how AI actually explores topics.

Query Fan Out Use Cases

How teams use Query Fan Out to understand AI's internal query logic

Coverage Expansion

Discover queries you should be tracking based on what AI models actually search for when researching your topics.

Semantic Analysis

Understand how AI connects topics in your space. See the web of related concepts that influence AI recommendations.

Source Discovery

Fan out queries often reveal what sources AI consults. See which websites and resources influence AI's understanding of your industry.

Competitive Intelligence

Discover what competitor-related queries AI generates when researching your brand. Understand the competitive context AI considers.

"After noticing rising visits from various LLMs in GA4, we needed visibility into those black boxes. LLM Pulse gave us the clarity to understand our brand's evolution and shape new strategies to boost our exposure."

Frequently Asked Questions

Learn how Query Fan Out reveals the queries AI uses internally.

What is Query Fan Out?

Query Fan Out extracts the related queries that ChatGPT generates internally when answering your tracked prompts. These include web search queries, tool calls, and source lookups, the actual queries the AI used to research and formulate its response.

How are the related queries discovered?

We capture them directly from the ChatGPT API response. The API returns structured data including web_search_results, search_results, tools, and sources, each containing the related queries AI used. This is real data, not predictions.

Is Query Fan Out a one-time analysis?

Fan out queries are extracted every time your prompts are executed. As you track more prompts and executions accumulate, you build a comprehensive picture of how AI models explore topics in your space.

How is this different from Prompt Research?

Query Fan Out extracts queries that AI models generate internally when answering. Prompt Research uses your website content and SEO data to suggest prompts before execution. Fan Out shows what AI thinks is related; Prompt Research shows what users might ask.

Can I add fan out queries to my tracking?

Yes. You can review all extracted fan out queries and choose which ones to add to your prompt tracking. This helps you systematically expand coverage based on what AI models actually consider relevant.