Answer: Few-shot Prompting

## 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.

Author: Ritesh Yadav