Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A developer is using the Gemini API to generate creative product taglines. The taglines are often bland and uncreative. The developer wants more variety and novelty in the outputs. Which parameter adjustment would most effectively increase the diversity of the generated taglines?
⚠ Common exam trap
Many exam-takers confuse temperature with top_p, incorrectly assuming that lowering top_p increases diversity, when in fact it restricts the token pool and reduces variety.
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 from 0.2 to 0.9.
Increasing temperature from 0.2 to 0.9 raises the randomness of token sampling, which directly increases the diversity and novelty of generated text. A low temperature (e.g., 0.2) makes the model highly deterministic, always picking the most probable next token, leading to bland outputs. A higher temperature (e.g., 0.9) allows less probable tokens to be selected more often, producing more creative and varied taglines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease top_p from 1.0 to 0.5.
Why it's wrong here
Lower top_p restricts the sampling pool, making outputs less diverse.
- ✗
Set frequency_penalty to 2.0.
Why it's wrong here
Frequency_penalty penalises tokens by how often they have already appeared, which curbs verbatim repetition rather than broadening creative choice; taglines can stay bland while repetition drops. It tempts because it is the obvious anti-repetition knob, and it would help when outputs loop the same phrases — a different problem from novelty.
- ✓
Increase temperature from 0.2 to 0.9.
Why this is correct
Temperature scales the sampling distribution's randomness; raising it from 0.2 to 0.9 flattens the probability curve, so lower-probability tokens are selected more often. That directly increases tagline variety and novelty rather than reinforcing the bland high-probability output.
- ✗
Decrease temperature from 0.7 to 0.2.
Why it's wrong here
Reducing temperature sharpens the probability distribution toward the most likely tokens, producing safer, more repetitive text and worsening blandness. It tempts because temperature is the best-known diversity control, and lowering it is right when you need consistent, reproducible, low-variance output such as structured extraction or classification.
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