Chain Of Thought Reasoning: Definition and Examples

Chain of Thought Reasoning is a fundamental approach in prompt engineering that replicates the human step-by-step thought process. Rather than asking a language model to directly produce an answer, it is prompted to explicit each step of its reasoning, creating a traceable logical chain from problem to solution.

This technique was formalized by Google researchers in 2022 in the paper "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" by Jason Wei et al. The study demonstrated that simply adding step-by-step reasoning examples in a prompt significantly improves the performance of large language models on arithmetic, logical, and common-sense reasoning tasks.

The principle is remarkably simple: instead of asking a question and expecting a direct answer, you guide the model to 'think out loud.' This can be done by adding an instruction like 'Reason step by step' (zero-shot CoT) or by providing examples of decomposed reasoning (few-shot CoT). The model then produces a sequence of intermediate steps that logically lead to the conclusion.

The effectiveness of Chain of Thought is explained by several mechanisms: it forces the model to allocate more computational capacity to the problem, reduces errors by making each step verifiable, and allows precise identification of where reasoning goes astray. This transparency makes it a valuable tool not only for obtaining better answers but also for understanding and auditing the AI's decision-making process.