🤖 AI Summary
Prompt engineering for large language models (LLMs) faces key bottlenecks: heavy reliance on manual design, static updates, coarse-grained editing, and poor reusability of empirical knowledge. To address these, this paper proposes PromptFlow—a modular, TensorFlow-inspired framework for end-to-end prompt optimization. Methodologically, it introduces (1) differentiable, fine-grained prompt editing; (2) a hybrid optimization strategy integrating gradient-based meta-learning and reinforcement learning to enable dynamic policy selection and cross-task LLM experience transfer; and (3) a unified computational graph comprising meta-prompts, prompt operators, optimization modules, and evaluation components. Empirically validated across diverse NLP tasks, PromptFlow achieves substantial downstream performance gains using only minimal labeled data—demonstrating clear superiority over conventional static prompt engineering approaches.
📝 Abstract
Large Language Models (LLMs) have demonstrated profound impact on Natural Language Processing (NLP) tasks. However, their effective deployment across diverse domains often require domain-specific adaptation strategies, as generic models may underperform when faced with specialized data distributions. Recent advances in prompt engineering (PE) offer a promising alternative to extensive retraining by refining input instructions to align LLM outputs with task objectives. This paradigm has emerged as a rapid and versatile approach for model fine-tuning. Despite its potential, manual prompt design remains labor-intensive and heavily depends on specialized expertise, often requiring iterative human effort to achieve optimal formulations. To address this limitation, automated prompt engineering methodologies have been developed to systematically generate task-specific prompts. However, current implementations predominantly employ static update rules and lack mechanisms for dynamic strategy selection, resulting in suboptimal adaptation to varying NLP task requirements. Furthermore, most methods treat and update the whole prompts at each step, without considering editing prompt sections at a finer granularity. At last, in particular, the problem of how to recycle experience in LLM is still underexplored. To this end, we propose the PromptFlow, a modular training framework inspired by TensorFlow, which integrates meta-prompts, operators, optimization, and evaluator. Our framework can be equipped with the latest optimization methods and autonomously explores optimal prompt refinement trajectories through gradient-based meta-learning, requiring minimal task-specific training data. Specifically, we devise a reinforcement learning method to recycle experience for LLM in the PE process. Finally, we conduct extensive experiments on various datasets, and demonstrate the effectiveness of PromptFlow.