Authors - Maulana Amirul Adha, Maulana Paramaditya Ananta, Bayu Suhendry, Ria Rahma Nida, Eka Dewi Utari, Nur Athirah Sumardi Abstract - The challenge of generating accurate and contextually complete mod-els and prompts in Model-Driven Engineering (MDE) using Large Language Models (LLMs) is based on the current limitations in understanding the complex structured data. The significance of this issue lies at the heart of modern software development where MDE has taken the lead to advance development in the field moving towards with the aim of automating manual processes. To increase this automation, the application of LLMs holds the potential to reduce the manual effort and reduce human error involved in the process. To address this, we pro-pose a context-based prompt generation framework that integrates the techniques of Retrieval-Augmented Generation (RAG) with LLMs such as GPT-4 and CodeLlama to produce prompts that are contextually accurate and sound. Along with these LLMs, tools like FAISS, LangChain, and PlantUML are also em-ployed to produce detailed and structurally accurate UML models and prompt to enhance MDE understandability. In summary, the proposed framework aims to improve the accuracy and completeness of model generation by providing a con-textually correct prompt with a high level of accuracy and enhances the interpret-ability and ability of trust in AI-generated artifacts, creating the way for more efficient, automated, and user-friendly MDE processes.
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