TextGrad Tutorial - Optimize LLM Prompts, Math and Code

Learn how to optimize Large Language Model (LLM) prompts, mathematical equations, and code using TextGrad, a Python framework that implements backpropagation through text feedback. Explore five detailed examples demonstrating TextGrad's capabilities, from reducing hallucinations through logical reasoning to performing textual gradient descent on selected variables. Master the inner workings of LLM prompting concepts like losses, optimizers, and gradients while discovering how to optimize code, solve math equations, and develop optimal prompts through training data. Gain hands-on experience with practical applications of TextGrad's simple interface for implementing LLM-gradient pipelines, understanding the differences between DSPy and TextGrad, and learning advanced techniques like prompt finetuning and formatted LLM calls. Access to an LLM is required to follow along with the practical demonstrations.