Large Language Models Are Zero Shot Reasoners

Zero-shot prompting asks a question without giving the LLM any other information. It can be unreliable because a word might have multiple meanings. For example, if you ask an LLM to "explain the different types of banks" it might tell you about river banks. Few-shot prompting gives the LLM an example or two before asking the question. This gives the LLM more context so it can give you a better answer. It can also help the LLM understand what format you want the answer in. Chain-of-thought prompting asks the LLM to explain how it got its answer. This helps you understand the LLM's reasoning process, which is an important part of Explainable AI (XAI). Chain-of-thought prompting can also help the LLM give a better answer by thinking about different possibilities. These three methods can all help you get better results from LLMs by providing more context or instructions.