Textgrad: Automatic “Differentiation” via Text

To optimize the new generation of AI systems, we introduce TEXTGRAD, automatic differentiation via text. Here we use differentiation and gradients as a metaphor for textual feedback from LLMs. In this framework, each AI system is transformed into a computation graph, where variables are inputs and outputs of complex (not necessarily differentiable) function calls. The feedback to the variables (dubbed ‘textual gradients’ [25]) are provided in the form of informative and interpretable natural language criticism to the variables; describing how a variable should be changed to improve the system. The gradients are propagated through arbitrary functions, such as LLM API calls, simulators, or external numerical solvers.