> ## 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.

# Training Cutoff

> A training cutoff is the date beyond which a language model's training data does not extend.

*Core concept* · *AI Search Infrastructure*

## Definition

A training cutoff is the date beyond which a language model's training data does not extend. Events, brand repositioning, product launches, or changes that occurred after the cutoff are not represented in parametric memory and must be supplied through real-time retrieval or fine-tuning.

## Why It Matters for AI Search

Training cutoffs create predictable gaps and errors in AI brand representations. A brand that repositioned after the cutoff may be described using pre-reposition language. A brand that launched after the cutoff may have no parametric representation at all. Understanding cutoffs per platform explains why a brand appears differently across ChatGPT, Perplexity, and Google — each draws on different training data vintages and different retrieval architectures.

## Related Terms

<CardGroup cols={2}>
  <Card title="Parametric knowledge" href="/ai-search-glossary/parametric-knowledge" />

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

  <Card title="Knowledge conflict" href="/ai-search-glossary/knowledge-conflict" />

  <Card title="Training corpus" href="/ai-search-glossary/training-corpus" />

  <Card title="Knowledge cutoff" href="/ai-search-glossary/knowledge-cutoff" />
</CardGroup>

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