Prompt Engineering Techniques for Improved Model Interactions

Abstract

Prompt engineering has been one of the primary methodologies to improve the interaction of machine learning models, especially in natural language processing (NLP) systems. While recent advances in large language models have improved their flexibility in a broad variety of applications, the process of achieving best performance on multiple tasks and domains remains a challenge. The crux of this challenge lies in the creation and optimization of input prompts, which play a pivotal role in determining the quality and relevance of model responses. While there has been growing interest in prompt engineering, there is no large body of literature that extensively investigates the methodologies for better model interactions. This present study is an effort to bridge the gap by investigating the strategies for effective prompt generation and editing resulting in better model responses. This research investigates the effect of ordered and dynamic forms of prompts and seeks to maximize the accuracy, coherence, and contextual relevance of model responses. The research is meant to introduce new techniques of fine-tuning prompts through the use of domain expertise and model feedback loops, which could play a critical role in further developing the flexibility of such models across different applications in real-world situations. In addition, we discuss the potential for combining prompt engineering with other enabling methods, including reinforcement learning and few-shot learning, to enhance interactions that are more robust and scalable. Ultimately, this investigation is meant to advance the current state of language models by delivering concrete frameworks to researchers and engineers that can enhance the quality of interactions in machine learning and help develop more effective and explainable AI systems.