> ## 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.

# Weight (Model)

> In the context of language models, weights are the numerical parameters learned during training that encode the model's knowledge, associations, and behavior...

*Technical implementation* · *AI Search Infrastructure*

## Definition

In the context of language models, weights are the numerical parameters learned during training that encode the model's knowledge, associations, and behavioral patterns. They are stored in the model's neural network and determine how the model responds to any given input.

## Why It Matters for AI Search

Model weights are where brand knowledge lives in LLMs. A brand that was well-represented in training data has its entity, associations, and attributes encoded in the model's weights — accessible without retrieval. A brand absent from training data has no weight-based representation and depends entirely on real-time retrieval to appear in AI responses. Understanding weights helps explain why different AI models represent the same brand differently, and why training data presence is a long-term brand asset.

## Related Terms

<CardGroup cols={2}>
  <Card title="Training corpus" href="/ai-search-glossary/training-corpus" />

  <Card title="Pre-training" href="/ai-search-glossary/pre-training" />

  <Card title="LLM brand memory" href="/ai-search-glossary/llm-brand-memory" />

  <Card title="Foundation model" href="/ai-search-glossary/foundation-model" />

  <Card title="Inference" href="/ai-search-glossary/inference" />
</CardGroup>

## Relevant Plate Lunch Collective Services

[AI SEO](https://www.platelunchcollective.com/services/ai-seo)  [Entity SEO](https://www.platelunchcollective.com/services/entity-seo)