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?

A single prompt for all products would likely lead to generic or inconsistent descriptions requiring significant manual editing, which is inefficient and counterproductive for specific needs.

Creating prompts per product category that highlight key features and specify the desired output format allows the LLM to generate targeted, consistent, and concise descriptions relevant to specific product groups. This approach provides sufficient context without being overly verbose, guiding the model to focus on critical features.

Including a diverse range of features in each prompt might make the descriptions overly broad, less concise, and potentially dilute the focus on specific, important features.

While detailed, product-specific prompts ensure precision, they might lead to very lengthy and less concise descriptions, potentially overcomplicating the input for generating just "concise, feature-specific" outputs.