> ## Documentation Index
> Fetch the complete documentation index at: https://wiki.platelunchcollective.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Informational Query

> An informational query seeks an explanation or description of a concept, decomposing minimally with shallow fan-out.

*Core concept* · *AI Search Infrastructure*

## Definition

An informational query seeks an explanation or description of a concept. It decomposes minimally — the model may generate one or two sub-queries to ground the answer, but the fan-out is shallow. These queries are most vulnerable to parametric knowledge dominating because the model often has a high-confidence answer and retrieval is confirmatory rather than generative.

## Why It Matters for AI Search

For well-established topics, a model answering an informational query from parametric knowledge will not retrieve anything — which means retrieval optimization does not help. Parametric presence — Wikipedia, widely-cited publications, training data representation — is the lever. For newer topics or repositioned brands where parametric knowledge is absent or wrong, retrieval content provides the correction.

## Related Terms

<CardGroup cols={2}>
  <Card title="Query decomposition" href="/ai-search-glossary/query-decomposition" />

  <Card title="Parametric knowledge" href="/ai-search-glossary/parametric-knowledge" />

  <Card title="Retrieval trigger" href="/ai-search-glossary/retrieval-trigger" />

  <Card title="Navigational query" href="/ai-search-glossary/navigational-query" />

  <Card title="Comparative query" href="/ai-search-glossary/comparative-query" />
</CardGroup>

## Relevant Plate Lunch Collective Services

[Entity SEO](https://www.platelunchcollective.com/services/entity-seo)  [Citation-Ready Content](https://www.platelunchcollective.com/services/citation-ready-content)  [AI SEO](https://www.platelunchcollective.com/services/ai-seo)