ReAct

Yao et al., 2022 (opens in a new tab) introduced a framework where LLMs are used to generate both reasoning traces and task-specific actions in an interleaved manner. Generating reasoning traces allow the model to induce, track, and update action plans, and even handle exceptions. The action step allows to interface with and gather information from external sources such as knowledge bases or environments.

The ReAct framework can allow LLMs to interact with external tools to retrieve additional information that leads to more reliable and factual responses.

Image Source: Yao et al., 2022 (opens in a new tab)

Full example coming soon!