This research paper investigates the theoretical foundations of Chain-of-Thought (CoT) prompting, a technique used to improve the reasoning abilities of large language models (LLMs). The authors demonstrate that incorporating all previous reasoning steps in the model training process, a concept they call "Coherent CoT," leads to better error correction and prediction accuracy compared to a step-by-step approach ("Stepwise ICL"). They then analyze the model's sensitivity to errors introduced at different stages of the reasoning process and find that CoT is more susceptible to errors in intermediate reasoning steps than to inaccuracies in final outcomes. Based on this insight, they propose a novel approach to enhance CoT performance by including both correct and incorrect reasoning paths in demonstration examples, allowing the model to learn from its mistakes and improve its overall reasoning ability.