NCA-GENM Question 188
Select 3You are working with a multimodal generative AI model to create product descriptions for an e-commerce platform. The initial prompt you used, 'Describe this product,' generates generic and uninformative outputs. Which of the following prompt engineering strategies would most effectively improve the quality and specificity of the generated descriptions?
- A
Include clear instructions and examples in the prompt to guide the model's response.
- B
Use broad and open-ended language in the prompt to allow for creative freedom.
- C
Incorporate relevant keywords and context, such as product type, features, and target audience, into the prompt.
- D
Add a system-level instruction, such as 'You are a professional copywriter,' to guide the model's tone and style.
- E
Reduce the length of the prompt to minimize processing time and avoid overloading the model.
Show answer and explanation
Correct answers: A, C, D
Explanation
Effective prompt engineering involves providing clear instructions, context, and examples while leveraging system-level instructions to guide the model's behavior. These strategies help ensure that the generative AI model produces high-quality, specific, and relevant outputs tailored to the desired use case.
- A. Correct.
Providing clear instructions and examples helps the model understand the desired output structure and style, leading to more relevant and accurate responses.
- B. Incorrect.
Using broad and open-ended language may result in generic or irrelevant outputs, as it does not provide sufficient guidance to the model.
- C. Correct.
Incorporating relevant context, like product type, features, and target audience, ensures the generated description is tailored and specific to the task.
- D. Correct.
Adding a system-level instruction, such as defining the model's role, can influence tone and style, aligning the output with the desired professional or creative standard.
- E. Incorrect.
While reducing the length of the prompt may save processing time, it typically sacrifices clarity and specificity, leading to less informative outputs.