## 1 Introduction  
Regular physical activity provides significant health benefits [1, 2, 3], and exercise and training prescriptions serve as fundamental components of health promotion and athletic performance optimization [4, 5]. They are inherently tailored to the diverse needs, goals, and physiological conditions of a wide range of populations, including athletes, either able-bodied or with disabilities, individuals managing chronic conditions, the elderly, and those seeking general well-being [6]. As such, correctly prescribing exercise and devising proper training plans demand expertise and personalized strategies to address the unique characteristics and objectives of each individual [7, 8]. When well-designed and evidence-based, exercise and training prescriptions can enhance psychophysical health, optimize biomechanical, psychological, and physiological adaptations to improve performance, and reduce the risk of overuse injuries and adverse health effects [9]. Traditionally, they have relied on the expertise of sports scientists, physiotherapists, and exercise physiologists, using subjective assessments and empirical guidelines. However, the scalability and precision of these traditional methods face limitations, particularly in addressing diverse populations [10, 11] or incorporating rapidly evolving scientific evidence into practice [12]. In this context, the integration of artificial intelligence (AI) into healthcare and well-ness represents a groundbreaking innovation with far-reaching implications across various domains [13, 14, 15, 16], including sports and physical activity [17, 18, 19, 20]. AI has demonstrated significant potential to address the limitations of traditional exercise and training prescriptions, offering solutions that are not only scalable but also capable of delivering highly personalized and data-driven recommendations [21, 22]. Generative AI, in particular, represents a paradigm shift [23, 24, 25]. Encompassing large language models (LLMs) such as ChatGPT, Google Gemini (previously known as Google Bard), and Microsoft Copilot, generative AI has demonstrated exceptional capabilities in conversational interfaces, decision support systems, and personalized recommendations [26, 27, 28, 29]. These systems leverage extensive datasets to produce context-aware, adaptive, and human-like responses, making them increasingly valuable in areas requiring individualized guidance, such as exercise and training prescriptions. By combining advanced natural language processing (NLP) and machine learning (ML) techniques [30, 31], generative AI can dynamically generate exercise and training prescriptions that integrate user-specific inputs--such as age, fitness level and training status, health history, and even genetic predispositions--with evidence-based knowledge and sport-specific goals, tailoring recommendations to various timeframes, whether for a single training session, a structured program spanning weeks or months, or a long-term athletic development plan extending over several years. This would enable the creation of optimized plans customized to various objectives, including general health improvement, rehabilitation, and peak athletic performance [32, 33, 34, 35]. Moreover, generative AI has the potential to address critical gaps in accessibility, making professional-level guidance available to individuals who may lack access to specialized expertise or resources due to financial, geographical, or logistical barriers [36, 37, 38]. To date, no systematic appraisal exists of the potential applications of generative AI in exercise and training prescription. In light of these considerations, this study aims to critically evaluate the role of generative AI in transforming exercise and training prescription practices. It will explore the strengths, opportunities, and limitations of these technologies, with a particular focus on their application in advanced and high-performance settings.