Answer: Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

## Explanation **Option A is correct** because this approach uses **few-shot learning** or **in-context learning**, which is an effective prompt engineering strategy for classification tasks like sentiment analysis. By providing examples of text passages with their corresponding labels (positive/negative), you're teaching the LLM the pattern it needs to follow for the new text passage. **Why other options are incorrect:** - **Option B**: While providing context about sentiment analysis might be helpful, it doesn't give the LLM concrete examples to learn from. LLMs benefit more from demonstration than explanation. - **Option C**: Providing no context or examples is **zero-shot learning**, which is less effective for classification tasks where the model needs to understand the specific format and criteria for labeling. - **Option D**: Examples of unrelated tasks (like text summarization or question answering) would confuse the model rather than help it learn the specific sentiment analysis task. **Key Concept:** In prompt engineering for classification tasks, **few-shot learning** (providing examples with labels) is typically more effective than zero-shot or unrelated examples because it demonstrates the exact pattern the model should follow.

Author: Ritesh Yadav