ReAct prompting is a method for prompting LLMs intended to improve the accuracy of their responses over competing methods such as chain-of-thought (CoT). Introduced in a 2022 paper, it works by bringing together reasoning and action (hence ReAct). Such an approach helps make LLM responses more explainable and reduces hallucinations compared to CoT, giving prompters a better chance of getting what they want. LangChain was originally developed to support this style of prompting. Autonomous agents based on ReAct prompting have proven to be some of the most widely used applications of LLMs our teams have been building. Recently, OpenAI introduced function calling to its APIs to make ReAct and similar prompting styles easier to implement without resorting to external tools like LangChain. We're still in the early stages of defining this discipline, but so far, ReAct and its descendants have pointed the way to some of the most exciting applications of LLMs.
