Also known as: Chain of thought Prompting
Chain-of-thought prompting is a prompt engineering technique that asks a language model to work through a problem step by step before giving an answer. It is often used for tasks involving reasoning, calculation, planning, or complex decision-making, although the visible explanation may not fully reflect the model's internal processing.
A prompt engineering technique that encourages a large language model (LLM) to explain its reasoning, step by step. For example, consider the following prompt, paying particular attention to the second sentence: How many g forces would a driver experience in a car that goes from 0 to 60 miles per hour in 7 seconds? In the answer, show all relevant calculations. The LLM's response would likely: - Show a sequence of physics formulas, plugging in the values 0, 60, and 7 in appropriate places. - Explain why it chose those formulas and what the various variables mean. Chain-of-thought prompting forces the LLM to perform all the calculations, which might lead to a more correct answer. In addition, chain-of-thought prompting enables the user to examine the LLM's steps to determine whether or not the answer makes sense.