Generative AI Leader Fundamentals of Generative AI Practice Question
A retail company wants to use a generative AI model to create personalized product descriptions for thousands of items. They need the descriptions to be consistent in style and format, but they also want to avoid the model inventing false features. Which approach best balances consistency and accuracy?
⚠ Common exam trap
The trap here is thinking that fine-tuning alone ensures accuracy, when in fact prompt design and temperature settings are more direct controls for consistency and hallucination reduction.
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 a low temperature and provide a structured prompt with clear guidelines and examples.
Setting a low temperature makes the model's output more predictable and consistent, while a structured prompt with guidelines and examples steers the model to follow a specific style and format. This combination reduces the likelihood of hallucinated features because the model is constrained by explicit instructions and examples. Other approaches either increase variability or do not directly address accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a high temperature to encourage creativity and let the model generate unique descriptions for each item.
Why it's wrong here
A high temperature increases variability, which would lead to inconsistent styles and formats across product descriptions. It also raises the risk of the model inventing false features. This approach prioritizes creativity over consistency and accuracy, which is not what the company needs.
- ✗
Increase the max output tokens to allow the model to elaborate more on each product.
Why it's wrong here
Increasing max output tokens allows longer descriptions but does not improve consistency or reduce hallucinations. In fact, longer outputs may include more opportunities for the model to drift or invent details. This parameter controls length, not the balance between consistency and accuracy.
- ✗
Fine-tune the model on a dataset of existing product descriptions to learn the style.
Why it's wrong here
Fine-tuning can help the model learn a consistent style, but it does not prevent hallucination of features. The model may still generate plausible but false details. Without additional constraints like a low temperature or structured prompts, fine-tuning alone may not ensure accuracy. It is also more resource-intensive than prompt engineering.
- ✓
Set a low temperature and provide a structured prompt with clear guidelines and examples.
Why this is correct
A low temperature reduces randomness, making outputs more deterministic and consistent. A structured prompt with guidelines and examples further constrains the model to follow the desired style and format. This combination minimizes the risk of hallucinated features while maintaining uniformity across thousands of descriptions, directly addressing both requirements.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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