Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A marketing team uses a Gemini model to generate ad copy. They notice the outputs are repetitive and lack variety across multiple runs for the same prompt. They want more diverse creative options without sacrificing relevance. Which parameter adjustment should they make?
⚠ Common exam trap
It's easy for candidates to confuse length controls or greedy decoding with creativity controls, when temperature is the parameter that governs output diversity.
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
✓
Increase the temperature to allow more varied token sampling.
Increasing temperature broadens token sampling, yielding more varied creative outputs while still following the prompt. Lower temperature, top-k of 1, and reduced max output tokens all make outputs more deterministic or shorter, not more diverse. Temperature is the primary control for balancing creativity and coherence in ad copy generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set top-k to 1 so the model always picks the single most likely token.
Why it's wrong here
Top-k of 1 is greedy decoding, which makes output highly deterministic and repetitive. It is the opposite of what the team needs. This setting would produce nearly identical ad copy every time. Diversity requires sampling from a broader set of candidate tokens.
- ✗
Decrease the temperature to reduce repetition.
Why it's wrong here
Lowering temperature makes the model more deterministic and likely to repeat the same high-probability phrasing. It reduces diversity rather than increasing it. The team wants more variety, so this change would worsen the problem. Temperature should be increased, not decreased, for creative diversity.
- ✗
Reduce the max output tokens to force the model to vary its wording.
Why it's wrong here
Max output tokens limits length and does not influence word choice or creativity. Shortening the output may truncate ideas rather than increase variety. This parameter controls how much text is generated, not how diverse it is. It will not solve the repetition problem.
- ✓
Increase the temperature to allow more varied token sampling.
Why this is correct
Higher temperature flattens the probability distribution, allowing less likely but still relevant tokens to be selected. This produces more diverse creative outputs across runs. The team should balance temperature to avoid incoherence while gaining variety. It directly addresses the repetition issue without changing the prompt or model.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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