AutoPDL: Automatic Prompt Optimization for LLM Agents

The performance of large language models (LLMs) depends on how they are prompted, with choices spanning both the high-level prompting pattern (e.g., Zero-Shot, CoT, ReAct, ReWOO) and the specific prompt content (instructions and few-shot demonstrations). Manually tuning this combination is tedious, error-prone, and non-transferable across LLMs or tasks. Therefore, this paper proposes AutoPDL, an automated approach to discover good LLM agent configurations. Our method frames this as a structured AutoML problem over a combinatorial space of agentic and non-agentic prompting patterns and demonstrations, using successive halving to efficiently navigate this space. We introduce a library implementing common prompting patterns using the PDL prompt programming language. AutoPDL solutions are human-readable, editable, and executable PDL programs that use this library. This approach also enables source-to-source optimization, allowing human-in-the-loop refinement and reuse. Evaluations across three tasks and six LLMs (ranging from 8B to 70B parameters) show consistent accuracy gains ($9.5\pm17.5$ percentage points), up to 68.9pp, and reveal that selected prompting strategies vary across models and tasks.

@article{spiess2025_2504.04365,
  title={ AutoPDL: Automatic Prompt Optimization for LLM Agents },
  author={ Claudio Spiess and Mandana Vaziri and Louis Mandel and Martin Hirzel },
  journal={arXiv preprint arXiv:2504.04365},
  year={ 2025 }
}