Chain-of-thought prompting is a technique that leads to the emergence of reasoning abilities in large language models, significantly increasing their performance by inducing them to articulate their reasoning steps before giving final answers to initial questions. This method is particularly useful when solving tasks where large language models frequently struggle, such as arithmetic math word problems, commonplace reasoning about physical or human interactions, or symbolic manipulations with letters, digits, or logical operators. There are several approaches to chain-of-thought prompting, including zero-shot, manual, and automatic methods, each with its own benefits and limitations. While larger LLMs perform better at reasoning tasks given chain-of-thought prompting, they still provide incorrect answers with the Zero-Shot-CoT method, which can be mitigated by applying other CoT methods. Chain-of-thought prompting is a powerful method to improve LLM performance on reasoning tasks by decomposing complex processes into simple steps for better accuracy, and various tools and platforms, such as VectorShift, are available to leverage this technique in AI applications.