AI-Powered Analytics in Technical Communication: Advancing Information Management Toward Content Data Science

We introduce a similarity-based ensemble framework for assessing the AI-readiness of topic-based technical content in Retrieval-Augmented Generation (RAG) contexts. Rather than evaluating RAG performance through query-and-answer measurements, the proposed approach characterizes content corpora through microscopic similarity metrics—mean similarity difference (MSD) and similarity width (SimWidth)—derived from vector representations of individual topics. These metrics quantify the distinctiveness and contextual precision of topic ensembles, providing a corpus-specific indicator of expected RAG behavior prior to deployment. The method is validated across four separately published industry-academic research projects in the PIAI!-Lab framework in different industrial domains. Results consistently confirm that modular, PI-Class-based information architectures with concise metadata improve ensemble-level AI-readiness, while variant-rich content without metadata pre-filtering reduces context precision. Beyond the technical findings, the paper demonstrates that Content Data Science methods can be effectively integrated into advanced academic research in technical communication, bridging information management and data science methodology.