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TEXTGRAD
Automatic differentiation through text — optimize LLM outputs with backpropagation
MIT
ABOUT
Optimizing LLM outputs currently relies on hand-tuning prompts or reinforcement learning. TextGrad brings automatic differentiation to text: it treats each LLM call as a differentiable operation in a computation graph. When you define a loss function on the output (e.g., "is this molecule synthetically feasible?"), TextGrad backpropagates textual gradients through the graph — improving prompts, chain-of-thought reasoning, or LLM-generated code in a principled, automated way rather than through manual iteration.
INSTALL
pip install textgradINTEGRATION GUIDE
1. Automatically optimize few-shot prompts by backpropagating through the prompt encoder
2. Improve chain-of-thought reasoning quality by tuning intermediate reasoning steps
3. Optimize LLM-generated code for correctness by defining test-passing as the loss function
4. Fine-tune agent behavior in multi-step pipelines where each step is an LLM call
5. Automatically discover better system prompts for classification and extraction tasks
TAGS
pythonpytorchllmoptimizationgradientbackpropagationauto-diff