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.Comment: 34 pages, 10 figure