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    • Start here
    • Learning paths5 paths
    • Modules11 modules
    • Projects5 projects
    • Quizzes
    • Flashcards
    • Homework
    • Interview prep8 question banks
    • Cheat sheets11 sheets
    • Glossary239 terms
    • Code snippets
    • Ask the mentor
    • My notes
    • My progress
  1. Reference
  2. Sampling

Glossary

Sampling

Choosing the next token from the model's probability distribution rather than always taking the top one. Settings such as temperature, top-k and top-p shape it.

Where it is explained

  • Sampling parameters and the model landscapeLLM foundations

See also

Terms explained in the same lessons.

  • API-only model
  • Batch invariance
  • Context window
  • Greedy decoding
  • Open-weight model
  • Temperature
Previous termRRFNext termSelf-attention

All terms, A to Z

GGenerative AI

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