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A zero-shot prompt combines an instruction that defines the task with a new input for the model to process, without providing any prior examples. For instance, to classify the sentiment of a negative text, the prompt would be structured as follows:

'Assume that the polarity of a text is a label chosen from {positive, negative, neutral}. Identify the polarity of the input.

Input: The traffic is terrible during rush hours, making it difficult to reach the airport on time.'

Example of a Zero-Shot Prompt for Polarity Classification (Negative Sentiment)

You are tasked with using a large language model to classify the sentiment of the following movie review. The model must choose a label from {positive, negative, neutral}. Write a complete, self-contained prompt that instructs the model to perform this task on the given input without providing any examples.

Input: 'This film was a masterpiece of storytelling and cinematography. I was captivated from beginning to end.'

Crafting a Zero-Shot Prompt for Sentiment Analysis

A developer is building a system to automatically categorize customer feedback. They want a large language model to label each piece of feedback with one of three specific labels: {positive, negative, neutral}. However, when they use the prompt below, the model often generates free-form answers like 'The customer is unhappy' or 'This is a bad review' instead of the required labels.

**Prompt Used:**
`Identify the sentiment of the following customer feedback.
Input: The shipping was incredibly slow, and the item arrived damaged.`

Analyze the developer's prompt. Explain the most likely reason why the model is not consistently returning one of the required labels and describe a specific modification to the prompt's instruction that would resolve this issue.

Diagnosing a Flawed Zero-Shot Prompt

A developer wants a large language model to determine the sentiment of a user's comment. The model should not be given any prior examples within the prompt itself, and the output must be one of the following labels: {positive, negative, neutral}. Which of the following prompts best follows these requirements?

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