Enhancing Prompt Engineering Using Model Driven Engineering Techniques

Enhancing Prompt Engineering Using Model Driven Engineering Techniques Model driven prompt engineering (mdpe) is an advanced approach that constructs prompts methodically from predefined software model such as a class diagram, ensuring they are structured and guided by models and meta models to optimize code generation through llms. Model driven prompt engineering published in: 2023 acm ieee 26th international conference on model driven engineering languages and systems (models) article #: date of conference: 01 06 october 2023.

Enhancing Prompt Engineering Using Model Driven Engineering Techniques Using a domain specific language (dsl), we define platform independent prompts that can later be adapted to provide good quality outputs in a target ai system. the dsl also facilitates managing prompts by providing mechanisms for prompt versioning and prompt chaining. This research article explores the core principles of prompt engineering, outlining its key components and the various prompt types that shape model behavior. To maximize the effectiveness of ai models, prompt engineering employs a variety of techniques tailored to different tasks and objectives. the following are several key techniques, each explained with examples of prompts designed to achieve specific outcomes. To fill this gap, we investigate existing techniques and applications of prompt engineering. we conduct a thorough review and propose a novel taxonomy that provides a foundational framework for prompt construction.

Enhancing Prompt Engineering Using Model Driven Engineering Techniques To maximize the effectiveness of ai models, prompt engineering employs a variety of techniques tailored to different tasks and objectives. the following are several key techniques, each explained with examples of prompts designed to achieve specific outcomes. To fill this gap, we investigate existing techniques and applications of prompt engineering. we conduct a thorough review and propose a novel taxonomy that provides a foundational framework for prompt construction. This paper proposes applying model driven engineering to support the prompt engineering process using a domain specific language (dsl), and defines platform independent prompts that can later be adapted to provide good quality outputs in a target ai system. The essence of prompt engineering lies in crafting the optimal prompt to achieve a specific goal with a generative model. this process is not only about instructing the model but also involves a deep understanding of the model’s capabilities and limitations, and the context within which it operates. This paper proposes an adaptive approach to prompt engineering that incorporates a feedback loop to enhance prompt specificity dynamically. our approach involves modular, context aware prompts that are adjusted based on feedback received from previous model responses. This study offers a comprehensive foundation and practical insights to advance prompt engineering research tailored to the complex and evolving needs of software engineering.

Enhancing Prompt Engineering Using Model Driven Engineering Techniques This paper proposes applying model driven engineering to support the prompt engineering process using a domain specific language (dsl), and defines platform independent prompts that can later be adapted to provide good quality outputs in a target ai system. The essence of prompt engineering lies in crafting the optimal prompt to achieve a specific goal with a generative model. this process is not only about instructing the model but also involves a deep understanding of the model’s capabilities and limitations, and the context within which it operates. This paper proposes an adaptive approach to prompt engineering that incorporates a feedback loop to enhance prompt specificity dynamically. our approach involves modular, context aware prompts that are adjusted based on feedback received from previous model responses. This study offers a comprehensive foundation and practical insights to advance prompt engineering research tailored to the complex and evolving needs of software engineering.