Toward Epistemic Stability: Engineering Consistent Procedures for Industrial LLM Hallucination Reduction

ArXi:2603.10047v1 Announce Type: cross Hallucinations in large language models (LLMs) are outputs that are syntactically coherent but factually incorrect or contextually inconsistent. They are persistent obstacles in high-stakes industrial settings such as engineering design, enterprise resource planning, and IoT telemes. We present and compare five prompt engineering strategies intended to reduce the variance of model outputs and move toward repeatable, grounded results without modifying model weights or creating complex validation models.