Highly effective, task-specific prompts are often heavily engineered by
experts to integrate detailed instructions and domain insights based on a deep
understanding of both instincts of large language models (LLMs) and the
intricacies of the target task. However, automating the generation of such
expert-level prompts remains elusive. Existing prompt optimization methods tend
to overlook the depth of domain knowledge and struggle to efficiently explore
the vast space of expert-level prompts. Addressing this, we present
PromptAgent, an optimization method that autonomously crafts prompts equivalent
in quality to those handcrafted by experts. At its core, PromptAgent views
prompt optimization as a strategic planning problem and employs a principled
planning algorithm, rooted in Monte Carlo tree search, to strategically
navigate the expert-level prompt space. Inspired by human-like trial-and-error
exploration, PromptAgent induces precise expert-level insights and in-depth
instructions by reflecting on model errors and generating constructive error
feedback. Such a novel framework allows the agent to iteratively examine
intermediate prompts (states), refine them based on error feedbacks (actions),
simulate future rewards, and search for high-reward paths leading to expert
prompts. We apply PromptAgent to 12 tasks spanning three practical domains:
BIG-Bench Hard (BBH), as well as domain-specific and general NLP tasks, showing
it significantly outperforms strong Chain-of-Thought and recent prompt
optimization baselines. Extensive analyses emphasize its capability to craft
expert-level, detailed, and domain-insightful prompts with great efficiency and
generalizability.

PromptAgent 是一个优化方法，可以自动产生与专家级相等质量的提示，基于蒙特卡罗树搜索实现了对专家级提示的策略性规划和精确的领域洞察。