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Generative AI Leader Using Gemini to generate marketing copy Practice Question
A company is using Gemini to generate marketing copy. They want the outputs to be more creative and varied. Which generation parameters should they adjust?
⚠ Common exam trap
Google often tests the misconception that increasing context length or adjusting a single parameter (like top-k) is sufficient for creativity, when in fact temperature and top-p must be increased together to achieve controlled randomness without generating gibberish.
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 temperature and adjust top-p to a higher value
Increasing temperature raises the randomness of token selection, making outputs more creative and varied, while adjusting top-p to a higher value (e.g., 0.9) allows the model to sample from a larger cumulative probability mass of likely tokens, further increasing diversity. Together, these parameters directly control the stochasticity of generation, which is essential for creative marketing copy.
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 temperature to 0 and top-k to 1
Why it's wrong here
Temperature 0 with top-k 1 forces greedy decoding, always selecting the single highest-probability token, which produces deterministic and repetitive output. It is tempting because these settings guarantee consistency, and they would be the correct choice for factual, reproducible tasks such as extracting structured data.
- ✗
Decrease temperature and increase top-k
Why it's wrong here
Lowering temperature sharpens the probability distribution toward likely tokens, and raising top-k widens the candidate pool but cannot restore the randomness that low temperature removes, so output becomes less varied. It is tempting because top-k does control sampling breadth, and this pairing would suit tasks needing focused, deterministic responses.
- ✓
Increase temperature and adjust top-p to a higher value
Why this is correct
Temperature scales the sampling distribution's randomness, while top-p restricts sampling to the smallest token set whose cumulative probability reaches the threshold. Raising both widens the candidate pool and flattens selection, directly producing the greater creativity and variation the stem requests.
- ✗
Increase the context window length
Why it's wrong here
Context window length governs how much input text the model can consider, not the randomness of token sampling, so it cannot increase creative variation. It is tempting because longer context genuinely helps with large prompts, and increasing it would be correct when the model must reference extensive source material.
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