TextGrad: Improving Prompting Using AutoGrad
Last year researchers from Stanford released DSPy, a framework for automatic self prompting, this framework replaces the tedious task of writing human prompts that are often sub-optimal. DSPy was the biggest breakthrough in the LLM space after RAG (Retrieval Augmented Generation). Now these amazing researchers are back with TextGrad. It is a powerful framework performing automatic “differentiation” via text.
TextGrad backpropagates textual feedback provided by LLMs to improve individual components of a compound AI system. In this framework, LLMs provide rich, general, natural language suggestions to optimize variables in computation graphs, ranging from code snippets to molecular structures. TextGrad showed effectiveness and generality across a diverse range of applications, from question-answering and molecule optimization to radiotherapy treatment planning. So, without further ado, let's go deeper into this awesome paper.
I would highly recommend checking out our DSPy blog before we go further into TextGrad.
Other prompting blogs: Self-Rewarding Language Model, Promptbreeder: Prompting LLMs in a Better Way, and Giving self-reflection capabilities to LLMs
