Chain-of-Thought Design

Chain-of-thought prompting asks the AI to show its reasoning step by step before arriving at an answer. When designed well, this produces more accurate, more nuanced, and more trustworthy outputs. When designed poorly, it produces verbose justification of bad answers.

When Chain-of-Thought Helps

• Complex reasoning: Multi-step problems where the answer depends on intermediate conclusions
• Ambiguous inputs: When the AI needs to consider multiple interpretations
• High-stakes outputs: When you need to verify the reasoning, not just the answer
• Creative exploration: When generating ideas benefits from building on each step
• Analytical tasks: Comparisons, evaluations, and trade-off analyses

When Chain-of-Thought Hurts

• Simple lookups: "What's the capital of France?" doesn't need step-by-step reasoning
• Speed-critical responses: Reasoning adds latency and token cost
• Pattern-matching tasks: Some tasks are better served by direct response
• When reasoning is wrong: The AI can reason convincingly toward an incorrect conclusion

Designing Reasoning Chains

A reasoning chain has structure. Design it deliberately: 1. Problem decomposition "First, break this problem into its component parts." 2. Evidence gathering "For each part, identify what you know and what you're uncertain about." 3. Analysis