A reference from Weave

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Build a practical understanding of language models, prompts, tokens, and context. Focus on the concepts that affect real applications and engineering workflows.

Terms beginning with G

6 terms

  • Generalization

    Generalization is a language-model concept about evaluation design and failure analysis. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Gradient accumulation

    Gradient accumulation is a language-model concept about evaluation design and failure analysis. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Gradient clipping

    Gradient clipping is a language-model concept about training behavior and measurement. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Grammar-constrained generation

    Grammar-constrained generation is a language-model concept about generation behavior and sampling. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Graph of thoughts

    Graph of thoughts is a language-model concept about representation and similarity. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Grouped-query attention

    Grouped-query attention is a language-model concept about serving behavior and operational tradeoffs. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals