Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A marketing team uses a text generation model to create ad copy. They want the output to be more diverse and creative, exploring unusual angles. Which parameter should they adjust?
⚠ Common exam trap
A common mix-up: candidates confuse length controls or hard sampling cutoffs with the smooth randomness control that temperature provides for creativity.
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 parameter that directly controls randomness in token selection. Increasing it flattens the probability distribution, making less likely words and ideas more probable, which yields more diverse and creative ad copy. While top-p and top-k also affect sampling, temperature is the primary and most intuitive knob for encouraging novel angles without changing other constraints.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Top-k
Why it's wrong here
Top-k restricts sampling to the k most likely tokens, which typically reduces diversity by cutting off the tail of the distribution. Lowering k makes output more predictable; raising k can add some variety but is less effective than temperature for creative exploration. It is a hard cutoff rather than a smooth scaling of probabilities.
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Max output tokens
Why it's wrong here
Max output tokens sets an upper bound on response length. It does not influence the randomness or creativity of token selection; it only truncates or allows longer responses. Increasing it may give more room for ideas but will not make the model explore unusual word choices or angles on its own.
- ✓
Temperature
Why this is correct
Temperature scales the logits before softmax, flattening the distribution at higher values. A higher temperature makes low-probability tokens more likely to be selected, producing more varied and creative text. This directly increases diversity and is the standard parameter for encouraging novel angles in ad copy while still remaining coherent.
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Top-p (nucleus sampling)
Why it's wrong here
Top-p controls the cumulative probability mass from which tokens are sampled. Lowering top-p makes output more focused; raising it can increase diversity but is less direct than temperature for creativity. It is a valid sampling control, but the primary parameter for boosting creative variation is temperature, which scales the entire probability distribution.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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