Landscape and Taxonomy of Prompt Engineering Patterns in Software Engineering

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Abstract
Advancements in large language models (LLMs) have enhanced their ability to handle ambiguous user instructions. However, effective prompt patterns remain crucial for usability and comprehension. This article presents a taxonomy of prompt engineering patterns for software engineering. It is based on a systematic literature review that was conducted in early 2023, when LLMs still faced significant limitations in context length and inference capabilities. Our study explores techniques that enhance the usability and reliability of LLMs, emphasizing the ongoing importance of well-designed prompts in optimizing task performance. Our findings highlight the critical role of prompt patterns in maximizing LLM’s potential, even as their capabilities continue to evolve.
Funding Information
  • KAKENHI (21KK0179, 23K18470)
  • Japan Science and Technology Agency (JPMJMI20B8)