What is chain-of-thought (CoT) prompting and when is it most beneficial?
Select an answer to reveal the explanation.
Short Explanation
Chain-of-thought is like asking someone to show their work on a math test. You ask Claude to reason step by step before the final answer, and that process often helps it get complex tasks right.
Full Explanation
Chain-of-thought prompting encourages the model to generate intermediate reasoning steps before producing a final answer. Asking Claude to show its reasoning step-by-step before giving the final answer, improving accuracy on complex reasoning tasks, is correct. Adding 'Think step by step' or 'Let's think through this carefully' often dramatically improves performance on math, logic, multi-step reasoning, and planning tasks. The reasoning process itself seems to improve the quality of the final answer. Linking multiple Claude API calls together in a pipeline describes API orchestration or chaining, not CoT. Providing very long, detailed context to Claude before asking a question describes grounding, not CoT. Chaining multiple user messages in a single API call describes multi-turn conversation structure, unrelated to CoT.