Optimizing interactions: Strategies for prompt engineering in large language models

Abstract

This manuscript delineates an innovative investigation into the rapidly evolving domain of prompt engineering, an essential competency in the contemporary landscape of sophisticated artificial intelligence, particularly concerning Large Language Models (LLMs) such as ChatGPT. Prompt engineering, defined as the meticulous formulation of precise and impactful prompts, is instrumental in directing LLMs to conform to explicit parameters, facilitate intricate procedures, and uphold the integrity of both the quality and quantity of their generated outputs. We present a groundbreaking aggregation of prompt engineering methodologies, systematically articulated as discrete patterns. These patterns bear resemblance to the notion of design patterns within software engineering, providing versatile and adaptable solutions to prevalent challenges encountered during interactions with LLMs. Our investigation elucidates a variety of frameworks for prompt engineering, illuminating their capacity to tackle a diverse array of issues faced in information retrieval operations. We additionally investigate a range of pattern-oriented methodologies that have been demonstrated to provoke augmented responses from AI models. This manuscript aspires to deliver a thorough compendium of these prompt engineering paradigms, presenting invaluable insights and pragmatic strategies that will enable users to fully leverage the potential of their engagements with large language models (LLMs), thereby making a substantial contribution to the domain of AI communication.