Towards a Catalog of Prompt Patterns to Enhance the Discipline of Prompt Engineering
To harness the full potential of LLMs in such crucial contexts, there needs to be a systematic, disciplined approach to "prompt engineering" that guides interactions with and evaluations of these LLMs.
Abstract
The rapid advent of Large Language Models (LLMs), such as ChatGPT and Claude, is revolutionizing various fields, from education and healthcare to the engineering of reliable software systems. These LLMs operate through "prompts," which are natural language inputs that users employ to query and leverage the models' capabilities. Given the novelty of LLMs, the understanding of how to effectively use prompts remains largely anecdotal, based on isolated use cases. This fragmented approach limits the reliability and utility of LLMs, especially when they are applied in mission-critical software environments. To harness the full potential of LLMs in such crucial contexts, therefore, we need a systematic, disciplined approach to "prompt engineering" that guides interactions with and evaluations of these LLMs.