After creating a foundation model in Einstein Studio, which hyperparameter should An Agentforce use to adjust the balance between consistency and randomness of a response?

Question

Options

• APresence Penally
• BVariability
• CTemperature

• A
  6% (2)
• B
  3% (1)
• C
  91% (29)

Explanation

Temperature is the hyperparameter that controls the trade-off between determinism (consistency) and creativity (randomness) in LLM outputs. A low temperature (near 0) makes responses more predictable and focused; a high temperature produces more varied and creative outputs. In Einstein Studio, this is the primary knob for tuning response style. Option A ('Presence Penalty') influences whether the model repeats topics it has already mentioned - it shapes diversity of topics, not the core consistency-vs-randomness balance. Option B ('Variability') is not a standard hyperparameter name in LLM configuration.

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