Explanation

Few-shot Prompting is the correct answer because:

• Few-shot prompting involves providing the model with a small number of examples ("shots") before asking it to perform the main task
• In this scenario, the developer provides two example inputs and outputs before the main query
• These examples serve as demonstrations that help the model understand the pattern and format expected for the classification task
• The examples show the model how to map text reviews to "positive" or "negative" classifications

Comparison with other options:

• Zero-shot Prompting (A): No examples are provided - the model must understand and perform the task based solely on the instruction
• Negative Prompting (C): Typically refers to specifying what NOT to include in generated content, not relevant to this classification scenario
• Guided Prompting (D): Not a standard prompting technique in machine learning terminology

This approach is particularly useful when you want the model to learn from limited examples without requiring extensive training data.