StraGo: Harnessing Strategic Guidance for Prompt Optimization

TL;DR

StraGo is a novel prompt optimization method that reduces prompt drifting by leveraging insights from both successful and failed cases, using in-context learning to provide detailed strategies, and demonstrating superior performance across diverse tasks.

Contribution

Introduces StraGo, a strategic-guided approach that mitigates prompt drifting and enhances prompt optimization using in-context learning and insights from success and failure cases.

Findings

Achieves state-of-the-art performance in prompt optimization.

Demonstrates stability and effectiveness across multiple tasks.

Outperforms existing prompt engineering methods.

Abstract

Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt effectiveness, they often lead to prompt drifting, where newly generated prompts can adversely impact previously successful cases while addressing failures. Furthermore, these methods tend to rely heavily on LLMs' intrinsic capabilities for prompt optimization tasks. In this paper, we introduce StraGo (Strategic-Guided Optimization), a novel approach designed to mitigate prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. StraGo employs a how-to-do methodology, integrating in-context learning to formulate specific, actionable strategies that provide detailed, step-by-step guidance for prompt optimization. Extensive experiments conducted across a range of tasks, including reasoning, natural language understanding, domain-specific knowledge, and industrial applications, demonstrate StraGo's superior performance. It establishes a new state-of-the-art in prompt optimization, showcasing its ability to deliver stable and effective prompt improvements.