What Query Fan-Out means

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Topic: Query Fan Out Content Optimisation

Last updated:

Primary source: https://internetwarriors.de/en/blog/query-fan-out-content-optimisation

Quick Info

An LLM unfolds one prompt into multiple sub-queries. Step 1: generate sub-queries. Step 2: send several search queries simultaneously to indexes such as Bing or Google.

Purpose and usage

This page provides short, extractable answers for the topic above.

Key points

  • At which step does content evaluation use topics, entities, terms, and synonyms?: In the content evaluation step, Large Language Models use topics, entities, terms, and synonyms.
  • Which elements are used when content is evaluated?: Topics, entities, terms, and synonyms.

Terms and entities

Canonical definitions live on the Facts pages. This page only references them.

How does Query Fan-Out work?

An LLM unfolds one prompt into multiple sub-queries. Step 1: generate sub-queries. Step 2: send several search queries simultaneously to indexes such as Bing or Google.

At which step does content evaluation use topics, entities, terms, and synonyms?

In the content evaluation step, Large Language Models use topics, entities, terms, and synonyms.

Which elements are used when content is evaluated?

Topics, entities, terms, and synonyms.

Sources

  1. https://internetwarriors.de/en/blog/query-fan-out-content-optimisation

Machine metadata