Ep 25. TextGrad: Automatic 'Differentiation' via Text

This episode delves into a paper exploring a novel approach to optimizing complex AI systems. The paper introduces TextGrad, a framework using textual feedback from LLMs to improve individual components of a compound AI system. TextGrad tackles the challenge of traditional automatic differentiation methods, which aren't suitable for optimizing complex AI systems with black box components. TextGrad leverages LLMs' reasoning capabilities to provide textual feedback analogous to gradients, guiding optimization. Examples include optimizing code snippets, problem-solving tasks, and refining LLMs in problem-solving. TextGrad is versatile and has been applied to various domains, including chemistry and medicine. The paper highlights its effectiveness, open-source nature, and potential to contribute significantly to AI research.