What is Chain of Thought (CoT)?
Chain of Thought (CoT) is a prompting technique that encourages large language models to break down complex reasoning tasks into explicit intermediate steps before arriving at a final answer. Instead of directly outputting an answer, the model is prompted to "show its work" by articulating its reasoning process step-by-step, similar to how humans solve complex problems by thinking through them incrementally.
This technique can be implemented in several ways: few-shot CoT provides examples that demonstrate step-by-step reasoning, while zero-shot CoT uses simple prompts like "Let's think step by step" to trigger the reasoning behavior. Research has shown that CoT prompting significantly improves performance on tasks requiring arithmetic, commonsense reasoning, and logical deduction, particularly for larger language models that have sufficient capacity to engage in multi-step reasoning.
Chain of Thought has become foundational in agent systems and complex problem-solving applications. It not only improves accuracy but also makes the model's reasoning process more transparent and debuggable. Many agent frameworks incorporate CoT-like patterns where the agent explicitly plans, reasons about, or reflects on its actions, building on the insight that externalizing intermediate reasoning steps leads to better outcomes in complex tasks.