  Prompt engineering for large language models (LLMs) is often a manual
time-intensive process that involves generating, evaluating, and refining
prompts iteratively to ensure high-quality outputs. While there has been work
on automating prompt engineering, the solutions generally are either tuned to
specific tasks with given answers or are quite costly. We introduce GRAD-SUM, a
scalable and flexible method for automatic prompt engineering that builds on
gradient-based optimization techniques. Our approach incorporates user-defined
task descriptions and evaluation criteria, and features a novel gradient
summarization module to generalize feedback effectively. Our results
demonstrate that GRAD-SUM consistently outperforms existing methods across
various benchmarks, highlighting its versatility and effectiveness in automatic
prompt optimization.
