Chain of Thought (CoT) is a prompting technique that enhances the reasoning capabilities of large language models (LLMs) by encouraging them to articulate their reasoning process step by step. Instead of providing a direct answer, the model breaks down complex problems into smaller, more manageable parts, simulating human-like thought processes. This method is particularly beneficial for tasks requiring complex reasoning, such as math problems, logical puzzles, and multi-step decision-making. CoT can be implemented through prompting, where the model is guided to "think step by step," or it can be an automatic internal process in some models. CoT improves accuracy and transparency by providing a view into the model's decision-making.