AI-102 Implement generative AI solutions Practice Question
A company uses Azure OpenAI to generate product descriptions. They want to ensure that the descriptions are consistent in style and tone. Which strategy should they use?
⚠ Common exam trap
Many exam-takers confuse fine-tuning (option A) as the only way to enforce style, overlooking that few-shot learning is a lighter, more flexible method that achieves the same goal without retraining.
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
✓
Provide a few examples of desired style in the prompt (few-shot learning).
Few-shot learning (option B) is the correct strategy because it directly controls style and tone by providing examples of desired output within the prompt. This leverages the model's in-context learning ability without modifying the underlying model weights, making it ideal for enforcing consistency without the cost and complexity of fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model on a dataset of product descriptions.
Why it's wrong here
Fine-tuning is resource-intensive and may be overkill.
- ✓
Provide a few examples of desired style in the prompt (few-shot learning).
Why this is correct
Examples guide the model to mimic the style.
- ✗
Set max_tokens to a small value to limit output length.
Why it's wrong here
max_tokens does not control style.
- ✗
Increase the temperature to 1.0 for more creativity.
Why it's wrong here
Higher temperature increases variability, reducing consistency.
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