Abstract
The paper argues that the primary failure mode of enterprise generative AI is structural rather than model-related. Organizations already possess authoritative semantic definitions in schemas, semantic layers, data dictionaries, and conceptual models, yet AI systems frequently ignore these assets and instead generate responses from statistical patterns. The paper introduces Look Up Before You Make Up (LUBMU), a foundational architectural principle stating that AI systems should consult authoritative structure before generating any response. It presents the theoretical basis for this principle through the generation–verification asymmetry, identifies three architectural requirements—consulting authoritative semantic structures, preserving those structures during retrieval through Structured Context Retrieval (SCR), and validating outputs against known shapes—and demonstrates how these practices improve trustworthiness, accuracy, and governance. Drawing on enterprise case studies and recent industry evidence, the paper shows that schema-grounded generation consistently outperforms generation-first approaches while substantially reducing hallucinations. It concludes that semantic grounding is becoming foundational infrastructure for enterprise AI, particularly in regulated and safety-critical domains where authoritative knowledge must constrain generation.
