AWS Certified Cloud Practitioner

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.