Content and AI-Delivery Analytics of Semantically Enriched Content in Engine Manufacturing

Standard Retrieval–Augmented Generation (RAF) often fails in safety-critical manufacturing and reveals the inherent problem of additional and inappropriate retrieved context topics. In engine manufacturing, where topic-based documentation is widely variant-driven, semantic similarity no longer correlates with technical relevance, assembly procedures for engine variants are linguistically often almost identical yet operationally incorrect. Recent RAG systems, relying on the LLM’s ability to discern this context noise during the generation phase, might fail to account for this extreme content-wise overlap. This paper proposes a dual-optimization strategy: ensemble sizing and semantic level enhancement. Instead of relying solely on vector similarity, we partition the retrieval space into sub-ensembles and apply a metadata scoring function. Our findings demonstrate that this hybrid approach might help to transform RAG-based systems into more variant-appropriate delivery systems essential for safety-critical environments.