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About this paper
Kaiser Pister, Dhruba Jyoti Paul, Patrick Brophy, Ishan Joshi
The University of Wisconsin-Madison introduces PromptSet, a dataset of 61,448 unique developer prompts extracted from open-source Python projects, illuminating current programmatic prompting practices. Its analysis reveals common issues like typographical errors in an estimated 1 out of 8 prompts and leading/trailing whitespace in over 30% of prompts, underscoring the need for static analysis and unit testing in LLM-powered applications.
Key insights
- Prompts, as critical software artifacts, require robust software engineering practices, including static analysis and unit testing, to enhance the reliability and maintainability of LLM-driven applications.
- Real-world programmatic prompts often prioritize "Input Semantics" and "Output Customization – Persona," indicating developers' need for precise control over LLM behavior in application contexts.
- Despite extensive research into prompt engineering, advanced techniques show surprisingly low adoption rates in actual developer prompts within the analyzed dataset.
Summary by alphaXiv.
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