Toward Epistemic Stability: Engineering Consistent Procedures for Industrial LLM Hallucination Reduction
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 telemetry platforms. 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. These methods include: (M1) Iterative Similarity Convergence, (M2) Decomposed Model-Agnostic Prompting, (M3) Single-Task Agent Specialization, (M4) Enhanced Data Registry, and (...
| Authors | Brian Freeman, Adam Kicklighter, Matt Erdman et al. |
|---|---|
| Primary category | cs.SE |
| Published | 8 March 2026 |
| Last updated at source | 5 April 2026 |
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