Answer: Chain-of-thought prompting

## Explanation Chain-of-thought prompting is specifically designed to enhance response quality for complex problem-solving tasks that require detailed reasoning and step-by-step explanations. This technique works by: 1. **Encouraging step-by-step reasoning**: The prompt explicitly asks the LLM to "think step by step" or "show your reasoning process" 2. **Breaking down complex problems**: It helps the model decompose complex problems into smaller, manageable steps 3. **Improving accuracy**: By showing intermediate reasoning steps, the model is less likely to make logical leaps or errors 4. **Providing transparency**: The step-by-step process makes the model's reasoning transparent and easier to verify **Why other options are not correct**: - **Few-shot prompting (A)**: Provides examples but doesn't explicitly encourage step-by-step reasoning - **Zero-shot prompting (B)**: No examples provided, just a direct request without structured reasoning guidance - **Directional stimulus prompting (C)**: Provides hints or cues but doesn't systematically guide through a reasoning process Chain-of-thought prompting has been shown to significantly improve performance on complex reasoning tasks in LLMs by mimicking human problem-solving approaches.

Author: Ritesh Yadav