A junior developer is using a large language model to classify user reviews but is receiving inconsistent and overly conversational responses. Analyze the developer's prompt below, identify its primary weakness, and explain how you would rewrite it to reliably produce a single-word classification from the set: 'positive', 'negative', or 'neutral'.

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A simple prompt for polarity classification can be constructed by pairing a direct instruction with the text to be analyzed. The following is a complete example of such a prompt:

'Analyze the polarity of the following text and classify it as positive, negative, or neutral.

The service at the restaurant was slower than expected, which was a bit frustrating.'

Example of a Simple Prompt for Polarity Classification

A developer needs to use a language model to categorize customer feedback into one of three specific classes: 'positive', 'negative', or 'neutral'. Which of the following prompts is most effectively designed to ensure the model provides a reliable and constrained classification for the given text?

Troubleshooting a Polarity Classification Prompt

You are given the following customer review: 'The user interface is very intuitive, but the battery life is disappointing.' Write a complete, simple prompt that instructs a language model to classify the polarity of this review as 'positive', 'negative', or 'neutral'.

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