Generative AI Leader Fundamentals of Generative AI Practice Question
A marketing team is using a generative AI model to create ad copy. They want to control the model's creativity so that outputs are more focused and deterministic for a formal campaign. Which parameter should they adjust?
⚠ Common exam trap
Test-takers frequently confuse parameters that affect diversity (like top-p or top-k) with the primary control for determinism, which is temperature.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Temperature
Temperature is the key parameter for controlling randomness; lowering it makes output more deterministic and focused. This helps ensure ad copy is consistent and on-brand for a formal campaign. Other parameters affect length or token selection but do not directly control creativity in the same way.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Temperature
Why this is correct
Temperature directly controls the randomness of the model's output. Lowering the temperature makes the model more deterministic and focused, which is ideal for a formal campaign where consistency is key. It reduces the likelihood of creative but off-brand outputs, aligning with the need for controlled creativity.
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Max output tokens
Why it's wrong here
Max output tokens limits the length of the generated text, not its creativity or determinism. Adjusting this parameter would only truncate responses, potentially cutting off important content. It does not influence how the model selects words, so it cannot make outputs more focused or deterministic.
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Top-p (nucleus sampling)
Why it's wrong here
Top-p controls the cumulative probability threshold for token selection, but it is often used alongside temperature. Adjusting top-p alone can influence diversity, but for making outputs more deterministic and focused, temperature is the primary parameter. Top-p is better for balancing between coherence and diversity, not for strict determinism.
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Top-k sampling
Why it's wrong here
Top-k sampling restricts the model to consider only the top k most likely tokens at each step. While it can reduce randomness, it is a cruder method than temperature and may not provide the fine control needed for a formal campaign. Temperature is more commonly used to adjust creativity and determinism in a predictable way.
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