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?
Few-shot prompting supplies concrete exemplars of the target style and tone directly in the prompt, so the model infers the desired register and structure without retraining. This satisfies the consistency constraint more reliably than zero-shot instructions alone, since patterns are demonstrated rather than described.
Why this answer
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.
Exam trap
The trap here is that candidates often 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.
How to eliminate wrong answers
Option A is wrong because fine-tuning requires a large, curated dataset and retraining the model, which is overkill for simple style consistency and introduces risks of catastrophic forgetting or overfitting to narrow patterns. Option C is wrong because setting max_tokens to a small value only truncates the output length; it does not influence the style, tone, or content of the generated text. Option D is wrong because increasing temperature to 1.0 increases randomness and creativity, which would actually reduce consistency in style and tone, not enforce it.