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TextGrad uses automatic differentiation through text to optimize LLM prompts and outputs, contrasting with DSPy's programmatic approach and ProTeGi's iterative refinement. The paper demonstrates TextGrad's application in agentic radiotherapy planning, automating outer-loop tuning for complex tasks.
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