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A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J. and Schmidt, D.C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. arXiv:2302.11382 [cs]. [online] Available at: https://arxiv.org/abs/2302.11382

General Annotation #

Prompt engineering is an essential skill set for conversing effectively with large language models (LLMs) like ChatGPT. It involves crafting instructions to guide the model in generating outputs that adhere to specific rules, automate processes, and maintain desired qualities. This paper outlines a catalog of prompt engineering techniques, presented as reusable solutions or patterns, to solve common challenges in LLM interactions. These patterns serve as a knowledge transfer method, analogous to software patterns, providing a structured approach to optimize output generation and interaction with LLMs.

Methodologies Used #

The study introduces a framework for documenting prompt patterns, enabling their adaptation across various domains. This framework takes inspiration from software patterns, adapting their structure to the context of LLM output generation. Each prompt pattern is detailed with intent, context, motivation, key ideas, example implementations, and consequences, mirroring the traditional software pattern documentation but tailored to prompt engineering needs.

Key Contributions #

  • Framework for Prompt Patterns: Establishes a structured approach for documenting and sharing prompt engineering techniques, enhancing the reusability and adaptability of prompt strategies.
  • Catalog of Prompt Patterns: Presents a comprehensive catalog of 16 prompt patterns developed and applied successfully in LLM conversations, addressing diverse challenges from output customization to error identification and interaction enhancement.
  • Combination and Adaptation: Demonstrates how prompts can be built from multiple patterns, highlighting the benefits of combining different strategies for more complex prompt engineering tasks.

Main Arguments #

The paper argues for the significance of prompt engineering in maximizing the effectiveness of LLMs in various applications, from software development to educational tools. By systematizing prompt engineering through a catalog of prompt patterns, the study advocates for a more efficient, structured, and transferable approach to prompt design. This approach not only improves output quality but also facilitates the sharing of effective strategies within the community.

Gaps #

While the catalog presents a solid foundation for prompt engineering, it also opens avenues for further research, particularly in expanding the catalog with new patterns, refining existing ones, and exploring their applications in other domains. The study acknowledges the evolving nature of LLMs and the need for continuous adaptation of prompt patterns to keep pace with advancements in language model capabilities.

Relevance to Prompt Engineering & Architecture #

The development of a prompt pattern catalog marks a significant advancement in the field of prompt engineering, offering a systematic way to enhance interactions with LLMs. It underscores the importance of structured prompt design in unlocking the full potential of LLMs and sets the stage for future innovations in prompt engineering and LLM architecture.

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Updated on April 27, 2024