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arXiv · 2502.11560

A Survey of Automatic Prompt Engineering: An Optimization Perspective

Wenwu Li·Xiangfeng Wang·Wenhao Li·Bo Jin
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Abstract

The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates. While manual prompt engineering faces limitations in scalability, adaptability, and cross-modal alignment, automated methods, spanning foundation model (FM) based optimization, evolutionary methods, gradient-based optimization, and reinforcement learning, offer promising solutions. Existing surveys, however, remain fragmented across modalities and methodologies. This paper presents the first comprehensive survey on automated prompt engineering through a unified optimization-theoretic lens. We formalize prompt optimization as a maximization problem over discrete, continuous, and hybrid prompt spaces, systematically organizing methods by their optimization variables (instructions, soft prompts, exemplars), task-specific objectives, and computational frameworks. By bridging theoretical formulation with practical implementations across text, vision, and multimodal domains, this survey establishes a foundational framework for both researchers and practitioners, while highlighting underexplored frontiers in constrained optimization and agent-oriented prompt design.

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Wenwu Li, Xiangfeng Wang, Wenhao Li, Bo Jin. 2025-02-17. A Survey of Automatic Prompt Engineering: An Optimization Perspective. https://arxiv.org/abs/2502.11560

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