The research requires fine-tuning of GPT-4 primarily because general-purpose models have limited capability in interpreting multimodal industrial data and domain-specific terminology, which compromises the reliability of energy-saving suggestions. This study will utilize a large set of annotated industrial datasets—including operational records, energy usage logs, and expert recommendations—to train the model for better semantic understanding of manufacturing environments. Moreover, the model needs contextual memory to ensure consistency in multi-turn interactions, something GPT-3.5 fine-tuning does not currently support effectively. A fine-tuned GPT-4 will therefore be better equipped for industrial adaptation and interpretable decision-making.