Solving Structural Hallucination in Generative AI: The Structural Integrity Enforcement Pipeline (SIEP)

Large Language Models (LLMs) are fundamentally non-deterministic, often failing to maintain structural consistency and schema compliance in complex multi-lingual outputs—a phenomenon defined as "Structural Hallucination." This paper introduces the Structural Integrity Enforcement Pipeline (SIEP), a six-stage deterministic post-processing framework designed to ensure 100% schema compliance without the latency or cost of model re-querying. By utilizing semantic anchors, canonical header shielding, and hyper-isolated translation markers, the SIEP transforms probabilistic LLM responses into production-grade structured data. We demonstrate the efficacy of this architecture within the SAGE Geomancy engine, achieving zero structural regressions across 10 languages. This framework provides a scalable blueprint for building reliable, mission-critical applications on top of generative AI.