> ## 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.

# Embedding Drift

> Embedding drift is the movement of a passage's embedding vector away from a target retrieval cluster caused by the introduction of off-topic content.

*Core concept* · *AI Search Infrastructure*

## Definition

Embedding drift is the movement of a passage's embedding vector away from a target retrieval cluster caused by the introduction of off-topic content. A section that starts answering one question and pivots to address a second ends up positioned between clusters rather than inside either one.

## Why It Matters for AI Search

Embedding drift is why mixed-topic passages underperform structurally clean ones even when both contain good information. The drift is not a gradual weakening — it is a geometric repositioning. The vector ends up in a low-density region between clusters, far from the dense retrieval targets of both topics. The practical fix is the same as the structural advice: one question per section.

## Related Terms

<CardGroup cols={2}>
  <Card title="Semantic center of gravity" href="/ai-search-glossary/semantic-center-of-gravity" />

  <Card title="Semantic density" href="/ai-search-glossary/semantic-density" />

  <Card title="Topic coherence" href="/ai-search-glossary/topic-coherence" />

  <Card title="Embedding" href="/ai-search-glossary/embedding" />

  <Card title="Cluster" href="/ai-search-glossary/cluster" />
</CardGroup>

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