AWS Certified Cloud Practitioner
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A company wants to use a large language model (LLM) to generate concise, feature-specific descriptions for the company's products. Which prompt engineering technique meets these requirements?
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RRitesh
A
Create one prompt that covers all products. Edit the responses to make the responses more specific, concise, and tailored to each product.
B
Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.
C
Include a diverse range of product features in each prompt to generate creative and unique descriptions.
D
Provide detailed, product-specific prompts to ensure precise and customized descriptions.
Explanation:
Option B is correct because it provides the most effective prompt engineering technique for generating concise, feature-specific descriptions. By creating prompts for each product category that highlight key features and include desired output format and length, the LLM can generate targeted, consistent descriptions that meet the company's requirements. This approach ensures:
- Specificity: Each prompt focuses on a particular product category
- Conciseness: The desired length is specified in the prompt
- Feature-focused: Key features are highlighted in the prompts
- Consistency: Output format is standardized across descriptions
Option A is inefficient as it requires manual editing. Option C may generate creative but not necessarily concise or feature-specific descriptions. Option D could work but doesn't explicitly address conciseness through output length specification.
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