AWS Certified Cloud Practitioner

Explanation

Chain-of-thought prompting is the correct technique for encouraging LLMs to show intermediate reasoning steps when solving complex problems.

Key Differences:

Chain-of-thought prompting:

• Encourages the model to break down complex problems into intermediate steps
• Shows the reasoning process step-by-step before arriving at the final answer
• Particularly effective for mathematical problems, logical reasoning, and multi-step tasks
• Helps improve accuracy and transparency in problem-solving

Output refinement prompting:

• Focuses on improving or polishing the final output
• Typically involves asking the model to revise or enhance its response
• Doesn't specifically encourage showing intermediate reasoning steps

Why Chain-of-thought is correct for this scenario:

1. The research team needs the LLM to explain its reasoning steps
2. They're working with complex mathematical problems that require step-by-step analysis
3. Chain-of-thought prompting is specifically designed to make the model's thinking process transparent
4. This technique helps in debugging, understanding model behavior, and improving problem-solving accuracy

Example of Chain-of-thought prompting:

Instead of asking: "What is 15% of 200?" You would ask: "Let's think step by step. What is 15% of 200?"

The model would then respond with something like: "First, 15% means 15/100 = 0.15 Then, 0.15 × 200 = 30 So 15% of 200 is 30."

This approach is particularly valuable in Amazon Bedrock for research and development purposes where understanding the model's reasoning process is crucial.