## 4 Discussion
### The Role of Generative AI in Personalized Exercise Programs for Athletes  
[PERSON] et al. [44], [PERSON] and [PERSON] [45], [PERSON] et al. [46], [PERSON] [42], and [PERSON] et al. [48] collectively explore the capabilities and limitations of ChatGPT and other LLMs in generating training programs across diverse contexts. Despite differences in methodology, study design, and target populations, a common theme emerges: while AI-generated programs can align with foundational training principles, they often lack the depth, specificity, and adaptability required for personalized and high-performance training. A key insight from [PERSON] et al. [44] is that the granularity of user input significantly influences the quality of ChatGPT-generated training plans. Their study demonstrated that a six-week running program improved in quality when detailed user-specific data were provided. However, even the most refined AI-generated plans had critical gaps, particularly in health screening, training frequency progression, psychological skills training, and skill acquisition. This finding aligns with [PERSON] and [PERSON] [45], who assessed ChatGPT's accuracy in answering questions about core exercises. They found that while ChatGPT excelled in general knowledge responses, it struggled with individualized recommendations and contraindications--reinforcing [PERSON] et al. [44]'s conclusion that AI-generated outputs lack sufficient customization and real-time adaptability. [PERSON] [42] took this inquiry a step further by conducting a quasi-experimental study to compare AI-generated and human-designed alisthenics training programs. While the AI-generated program improved flexibility and upper extremity endurance in male participants, it was notably less effective than the human-made program in enhancing cardiovascular endurance and lower extremity strength. Female participants in the AI-generated group showed no significant improvements at all. These findings suggest that, while AI can assist in exercise programming, its efficacy varies across populations and fitness components--a limitation also noted by [PERSON] et al. [44], whose study revealed ChatGPT's shortcomings in monitoring contextual factors such as environmental conditions and individual load responses. Beyond individual studies, comparative analyses of different LLMs provide additional context. [PERSON] et al. [48] highlighted GPT-4's superiority over GPT-3.5 in designing resistance training programs, particularly in implementing advanced programming concepts such as block periodization. However, GPT-4 still fell short in accommodating real-time adaptability, sex-specific considerations, and emerging methodologies like blood flow restriction training. [PERSON] et al. [46] further explored LLM-based resistance training plans, comparing GPT-4 with Google Gemini. Their findings revealed significant variability in output quality, with GPT-4 producing more structured and evidence-based programs, especially when given detailed input. Google Gemini, in contrast, struggled to generate coherent hypertrophy training plans when minimal input was provided, emphasizing the importance of prompt specificity. Both studies highlight a broader issue: while AI can assist in structured programming, reproducibility, and consistency remain challenges, as identical prompts sometimes yield different outputs. Taken together, these studies paint a nuanced picture of AI's role in exercise programming. While LLMs like GPT-4 can generate structured plans based on established principles, their effectiveness is highly dependent on input granularity. These systems heavily depend on the development of robust methods and precisely engineered prompts, which play a crucial role in enhancing accuracy, ensuring safety, and aligning AI-generated recommendations with real-world physiological and biomechanical principles.