Understanding Chain-of-Thought Prompting in Large Language Models

In CoT prompting, the model is instructed to think sequentially by outputting intermediary steps of reasoning. This often involves a prompt that explicitly includes phrases like 'Let's think step by step', which signals the model to break down the task into a series of logical steps. The technique works by leveraging the model’s capacity for greedy decoding, where each predicted token is based on the highest probability outcome. For example, when solving simple mathematical problems, CoT prompting can lead the model to enumerate its reasoning—calculating intermediate values and mapping each reasoning step—before delivering the correct final answer^[1].

A clear example of CoT prompting involves a math problem: "When I was 3 years old, my partner was 3 times my age. Now, I am 20 years old. How old is my partner?" Without CoT prompting, the model might produce an incorrect answer. However, by including an instruction such as 'Let's think step by step', the model produces a series of reasoning steps. It computes the partner's age when the speaker was 3 (calculating 3 × 3), determines the age difference, and then adds the necessary years to arrive at the final answer of 26 years. This structured reasoning process not only provides a correct answer but also makes the internal logic transparent and interpretable^[1].

The main advantage of CoT prompting is that it improves the model's performance on tasks that require logical reasoning and problem solving. By generating intermediate reasoning steps, users can better understand how the final answer was derived and identify any potential pitfalls if the model's reasoning goes astray. Moreover, while this technique can increase the number of tokens generated and may result in higher computational costs, the trade-off is often worthwhile in terms of improved accuracy and reduced hallucinations. Additionally, CoT prompting offers robustness in transferring performance across different versions of models by concentrating on a reasoning process that remains relatively stable^[1].