AI-102 Implement generative AI solutions Practice Question
A company uses Azure OpenAI to generate product descriptions. They notice that the model occasionally produces descriptions that include false claims about product features. The company needs to reduce the frequency of these inaccuracies without changing the training data. Which parameter adjustment would be most effective?
⚠ Common exam trap
A common mix-up: candidates confuse temperature with creativity or length control, assuming that increasing randomness (higher temperature) or extending output length (max_tokens) will somehow improve accuracy, when in fact lower temperature is the standard parameter for reducing hallucinations.
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
✓
Decrease the temperature parameter
Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect statements. This directly addresses the need to reduce false claims without modifying training data, as lower temperature forces the model to rely on its most probable (and typically more accurate) token predictions.
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 top_p parameter
Why it's wrong here
Increasing top_p makes outputs more diverse, potentially increasing inaccuracies.
- ✗
Increase the max_tokens parameter
Why it's wrong here
Increasing max_tokens allows longer outputs but does not reduce false claims.
- ✓
Decrease the temperature parameter
Why this is correct
Lower temperature makes the model more focused and less likely to hallucinate.
- ✗
Increase the frequency_penalty parameter
Why it's wrong here
Frequency penalty reduces word repetition, not factual accuracy.
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