A Taxonomy of Single-Turn Textual Prompt Patterns

2026-06-29

AI Summary

This study addresses the lack of a unified and reproducible taxonomy for “prompt patterns” in existing research. Focusing on single-turn textual prompts, it proposes the first systematic classification comprising 30 distinct and well-defined prompt patterns, organized along two orthogonal dimensions. Through a comprehensive literature review, pattern identification, and taxonomic methodology, the work establishes a structured and reproducible knowledge framework. By standardizing terminology and definitions, this classification provides a foundational reference for prompt engineering, significantly enhancing comparability and reproducibility across related studies.

Abstract

Large language models (LLMs) are now widely employed in software development and everyday use. Interacting with LLMs requires crafting prompts, which range from simple ad hoc sentences to extensive, detailed, and structured instructions. Knowledge about prompt engineering has been documented in several surveys and catalogs in the literature. However, the term ``prompt pattern'' is defined differently across sources, and existing works have classified prompt patterns in different ways. In this report, we present a taxonomy of prompt patterns for single-turn, text-based interactions. Following a reproducible method, we identified 30 unique and canonical prompt patterns, organized along two dimensions.

Problem

• prompt pattern
• taxonomy
• large language models
• prompt engineering
• single-turn interaction

Innovation

• prompt patterns
• taxonomy
• large language models
• prompt engineering
• single-turn interaction