We discuss a technique called self-consistency which enhances the reasoning capabilities of large language models (LLMs). This technique involves prompting an LLM to generate multiple reasoning paths for a question and then selecting the most consistent answer among these paths. This method improves the accuracy and reliability of LLMs, particularly for tasks requiring complex reasoning, such as arithmetic and commonsense reasoning.