  Prompt engineering is a crucial yet challenging task for optimizing the
performance of large language models (LLMs) on customized tasks. This
pioneering research introduces the Automatic Prompt Engineering Toolbox (APET),
which enables GPT-4 to autonomously apply prompt engineering techniques. By
leveraging sophisticated strategies such as Expert Prompting, Chain of Thought,
and Tree of Thoughts, APET empowers GPT-4 to dynamically optimize prompts,
resulting in substantial improvements in tasks like Word Sorting (4.4%
increase) and Geometric Shapes (6.8% increase). Despite encountering challenges
in complex tasks such as Checkmate in One (-14.8%), these findings demonstrate
the transformative potential of APET in automating complex prompt optimization
processes without the use of external data. Overall, this research represents a
significant leap in AI development, presenting a robust framework for future
innovations in autonomous AI systems and highlighting the ability of GPT-4 to
bring prompt engineering theory to practice. It establishes a foundation for
enhancing performance in complex task performance and broadening the practical
applications of these techniques in real-world scenarios.
