Faragó, DavidHerrmann, Andrea2024-02-052024-02-0520230720-8928https://dl.gi.de/handle/20.500.12116/43480This paper demonstrates Prompt Engineering (PE) on a running example: generating unit test cases for a given function. By iter atively adding further prompt patterns and measuring the robustness, correctness, and comprehensiveness of the AI’s output, multiple prompt patterns and their purpose and strength are investigated. We conclude that high robustness, correctness, and comprehensiveness is hard to achieve, and many prompt patterns (single prompt as well as patterns that span over a conversation) are necessary. More generally, quality assurance is a dominant part of PE and closely intertwined with the development part of PE. Thus traditional testing processes and stages do not adequately apply to QA for PE, and we suggest a PE process that covers the development and quality assurance of prompts as alternative.enprompt engineeringtest generationLarge Language ModelsqualityText/Conference Paper