Semantic Information Architectures and Topic Ensemble Properties in AI Delivery of Product Information in the Domain of Water Treatment

This paper examines how semantic information architectures within technical communication shape topic ensemble properties being relevant for retrieval-augmented generation (RAG) systems. They allow for investigating the AI-readiness of technical content for AI-based delivery in the present domain of water treatment. The analysis characterizes the dependence of AI-driven delivery on similarity-based metrics and metadata completeness. Topic content management is operationalized within the PI-Class framework by analyzing topics with respect to content variants and versioning, and by assessing their implications for retrieval precision. Building on previous research in technical communication, the paper further investigates the role of large language models (LLMs) in supporting the content engineering phase through automated analysis of legacy documents, derivation of metadata classifications, and curation of the resulting semantic structures in knowledge graphs.