Fine-tuning GPT-4 is essential due to the highly specialized nature of machine tool performance evaluation, which involves technical jargon, structured metrics, and complex semantic relationships. GPT-3.5 lacks the domain-specific comprehension required to interpret engineering expressions such as “spindle thermal drift coupled with XY repeatability,” and cannot reliably correlate such terms with corresponding sensor data. Additionally, GPT-3.5’s temporal reasoning and parameter attribution capabilities are insufficient for generating logically sound engineering recommendations.In contrast, GPT-4 offers superior contextual memory, reasoning, and understanding. Through fine-tuning, it can learn the conventions of performance reports, common failure modes, and parameter distributions in this domain, enabling it to produce expert-level analyses that are technically accurate and practically actionable. Thus, GPT-4 fine-tuning is indispensable for this project.