The January 30, 2025 paper introduces **LLM-AutoDiff**, a novel framework for **Automatic Prompt Engineering (APE)** that allows for the optimization of complex Large Language Model (LLM) workflows. This framework models an entire LLM application—including multiple LLM calls, functional components like retrievers, and cyclical operations—as a **directed, auto-differentiable graph**. By treating textual inputs as trainable parameters, LLM-AutoDiff uses a separate "backward engine" LLM to generate **textual gradients** (feedback) that guide an optimizer LLM to revise prompts, effectively automating the manual and labor-intensive process of prompt engineering. The paper details several technical advances, such as **pass-through gradients for functional nodes** and **time-sequential gradients for cyclic structures**, to ensure accurate error attribution across multi-component pipelines, ultimately demonstrating improved accuracy and efficiency over existing textual gradient and few-shot baselines.Source:January 30, 2025LLM-AutoDiff: Auto-Differentiate Any LLM Workflowhttps://arxiv.org/pdf/2501.16673

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