Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A marketing team uses Gemini in Vertex AI to generate campaign taglines. The first drafts are generic and closely mirror the prompt wording. They want more distinctive, varied taglines from the same model without changing the model itself. Which parameter should they adjust?
⚠ Common exam trap
Candidates often confuse controls that change response length or sampling cutoffs with the primary diversity control, and mistakenly lowering temperature when the goal is more varied output.
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
✓
Raise the temperature to increase randomness and diversity in token selection.
Temperature is the direct control over how sharply the model favors likely tokens. Raising it flattens the distribution and encourages less predictable word choices, which produces more distinctive taglines from the same model. Lowering temperature increases repetition, output token limits only change length, and top-p is a related but secondary truncation control rather than the primary diversity knob.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the maximum output tokens to allow longer taglines.
Why it's wrong here
Maximum output tokens caps response length and does not affect which words the model chooses. Longer output can simply extend generic phrasing rather than make it more distinctive. The complaint is about blandness and echoing the prompt, not truncation, so raising the token limit leaves the stylistic problem untouched while potentially producing rambling text.
- ✗
Lower the temperature to make the output more deterministic.
Why it's wrong here
Lowering temperature makes the model pick more predictable, high-probability tokens, which produces safer and more repetitive text. That would push taglines even closer to common phrasing and the prompt itself, the opposite of the desired distinctiveness. Determinism is useful for consistency tasks, but here it suppresses the variety the team is trying to achieve.
- ✓
Raise the temperature to increase randomness and diversity in token selection.
Why this is correct
Temperature scales the probability distribution over next tokens; raising it flattens that distribution so less likely words become more probable. That yields more varied and less generic phrasing, which fits the goal of distinctive taglines. The model is unchanged, so this is a low-effort tuning knob. Very high values risk incoherence, so moderate increases are appropriate.
- ✗
Enable a higher top-p value to consider more cumulative probability mass.
Why it's wrong here
Top-p, or nucleus sampling, restricts sampling to the smallest set of tokens whose cumulative probability exceeds the threshold. Raising top-p widens that set and can modestly increase variety, but it is a truncation mechanism rather than the primary diversity control, and its effect here is indirect. Temperature is the direct lever for making output less generic, so this option is not the best fit.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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