Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A financial analyst uses generative AI to summarize earnings reports. The summaries vary in style. Which THREE methods can improve consistency? (Choose three.)
⚠ Common exam trap
A common misconception in this Google exam is that increasing max output tokens or enabling citation mode improves consistency. In reality, these features control length and attribution respectively, not stylistic uniformity. The correct methods focus on reducing randomness (low temperature), providing consistent examples (few-shot), or fine-tuning.
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
✓
Set temperature to 0.2
Setting temperature to 0.2 reduces randomness in token sampling, making the model more deterministic and less likely to produce stylistic variations. Lower temperatures (e.g., 0.1–0.3) narrow the probability distribution, forcing the model to select the most likely next token, which directly improves consistency across multiple summaries.
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 0.2
Why this is correct
Reduces output randomness.
- ✗
Increase max output tokens
Why it's wrong here
Controls length, not style.
- ✗
Enable citation mode
Why it's wrong here
Adds citations, does not affect style.
- ✓
Use few-shot prompting with fixed examples
Why this is correct
Provides consistent style guidance.
- ✓
Fine-tune on a curated dataset of desired summaries
Why this is correct
Adapts model to specific style.
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