In the world of machine learning, gradient descent is the engine of progress. It's the mathematical process that allows models to "learn" by incrementally minimizing their errors. But what if we could apply this powerful concept not just to numbers, but to words, prompts, and even code?

A fascinating framework called TextGrad does exactly that. It builds a powerful analogy that allows us to "train" text by translating the core principles of calculus-based optimization into a series of conversations with Large Language Models (LLMs).

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