Prompting Strategies for LLM-Based Cooperative Data Service Discovery

In the context of Smart Manufacturing and the Internet of Production, data service discovery plays a central role in enabling cross-organizational collaboration and data-driven innovation. Nevertheless, effective discovery and composition of data services often require deep technical knowledge, limiting the autonomy of domain experts and R&D managers in designing analytics workflows. This paper presents a cooperative approach to data service discovery that combines Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), leveraging a conceptual model of data services and analytics scenarios. On top of this model, a set of prompting strategies are designed to support different levels of user expertise and interaction goals. These strategies leverage the cooperative nature of the approach, enabling domain experts and R&D managers to incrementally build, extend, and refine analytics data service pipelines through the interaction with LLMs. We describe how these prompting strategies are tightly integrated with the RAG components to inject contextual knowledge derived from a catalog of data services and analytics scenarios. The system is implemented using open-source technologies and evaluated extensively in a real-world smart factory case study. Our evaluation includes both quantitative metrics (precision, recall, faithfulness, factual correctness) and a qualitative user study, demonstrating the effectiveness of prompting strategies and the feasibility of LLM-supported data service discovery in cooperative industrial settings.