With the widespread adoption of AI models like ChatGPT, performance instability leading to ‘degradation’ has become a major pain point for users. This article explores the limitations of existing detection tools and proposes solutions to determine if a model is underperforming through real-time identity checks or classic test questions. The author shares a real-world case: when a model incorrectly claimed to be GPT-4 instead of GPT-5.2, it caused troubleshooting difficulties and wasted significant time. The article emphasizes that AI model degradation not only affects user experience but can also lead to substantial work losses, calling for the development of more reliable real-time detection tools and clear alerts when models underperform to prevent user misdirection.
