Explanation

Few-shot Prompting is the correct answer because:

1. Definition: Few-shot prompting involves providing the model with a small number of examples (typically 2-10) before asking it to perform a task on new input.

2. Key characteristic: The developer provides "two example inputs and outputs before the main query" - this is the defining feature of few-shot prompting.

3. Contrast with other options:

   • Zero-shot Prompting: No examples are provided; the model must understand the task from the prompt alone.
   • Negative Prompting: Typically refers to specifying what NOT to include in generated content, not providing examples.
   • Guided Prompting: Not a standard term in prompt engineering; may refer to structured prompting but doesn't specifically mean providing examples.

4. Application: In text classification tasks like sentiment analysis (positive/negative reviews), few-shot prompting helps the model understand the desired format and criteria by showing concrete examples.

Why this approach works: By providing examples, the model learns the pattern and can apply similar reasoning to new, unseen text reviews, improving accuracy compared to zero-shot approaches.