Chain of Thought

Chain of Thought (CoT) is a prompting technique that dramatically improves AI model performance on complex reasoning tasks by encouraging the model to "think out loud" — showing its step-by-step reasoning process before arriving at a final answer.

Without CoT, models often jump directly to answers and make mistakes on problems requiring multiple reasoning steps. With CoT, the model breaks the problem down, works through each step explicitly, and arrives at more accurate conclusions.

There are several variants: zero-shot CoT (simply adding "Let's think step by step" to a prompt), few-shot CoT (providing examples of step-by-step reasoning for similar problems), and self-consistency CoT (generating multiple reasoning paths and selecting the most common answer).

Chain of thought is particularly effective for: mathematical reasoning ("If a store has 45 apples and sells 3/5 of them..."), logical deduction ("Given that all A are B, and some B are C..."), multi-step planning ("Plan a trip that includes..."), causal reasoning ("Why did X happen?"), and complex analysis ("Compare the trade-offs between...").

For AI agent builders, CoT is valuable because many real-world tasks require reasoning. An agent helping with business decisions, qualifying leads, or diagnosing issues benefits from explicit reasoning steps. System prompts can encourage CoT by instructing the agent to "think through the problem step by step before responding."

Modern models like OpenAI's o1 and o3 have CoT built into their architecture, automatically using extended reasoning before generating responses.