ArXiv Preprint – Prompt Engineering a Prompt Engineer

In this episode we discuss Prompt Engineering a Prompt Engineer
by Qinyuan Ye, Maxamed Axmed, Reid Pryzant, Fereshte Khani. The paper presents PE2, an advanced method for automatically engineering prompts for large language models (LLMs), enabling them to perform better at complex tasks. By incorporating elements like a step-by-step reasoning template and verbalized optimization concepts (akin to batch size and momentum), PE2 significantly improves LLMs’ task performance, surpassing previous methods on various datasets. The versatility and effectiveness of PE2 are demonstrated through successful applications across different benchmarks, including the Instruction Induction benchmark and real-world industrial prompts, with the method showing a strong ability to refine and correct existing prompts.