Generative AI for synthetic data generation has emerged as a critical solution for addressing data scarcity and privacy concerns, particularly in specialized and private domains. This paper explores advanced methods for generating task-specific training data using large language models (LLMs), focusing on prompt engineering, parameter-efficient adaptation, and data quality measurement. It discusses the challenges and future directions of synthetic data generation, emphasizing its potential in low-resource tasks and specialized applications like healthcare. Recent advancements in LLMs, such as GPT-3, LLaMA, and ChatGPT, have significantly enhanced the capabilities of generative AI, enabling the creation of realistic synthetic data. These models, trained on vast datasets, can generate coherent and contextually relevant text, making them valuable for data augmentation in various fields. Prompt engineering techniques, such as attribute-controlled prompts and verbalizers, are crucial for generating diverse and task-specific synthetic data. Parameter-efficient methods, including prefix tuning and LoRA, allow LLMs to be adapted for specific tasks without extensive retraining. The paper also addresses the challenges of synthetic data generation, including ensuring data quality, mitigating biases, and addressing ethical concerns. Synthetic data can be used to overcome the limitations of real data in low-resource and long-tail scenarios, enhancing model performance and enabling faster deployment. In healthcare, synthetic data has shown promise in tasks like medical imaging analysis and named entity recognition, where real data is scarce or sensitive. Despite these advancements, challenges such as hallucination, data privacy, and ethical implications remain significant. Future research should focus on improving data quality, developing robust ethical frameworks, and exploring new methods for synthetic data generation and application. The integration of LLMs in synthetic data generation not only enhances AI capabilities but also promotes responsible and inclusive AI development.Generative AI for synthetic data generation has emerged as a critical solution for addressing data scarcity and privacy concerns, particularly in specialized and private domains. This paper explores advanced methods for generating task-specific training data using large language models (LLMs), focusing on prompt engineering, parameter-efficient adaptation, and data quality measurement. It discusses the challenges and future directions of synthetic data generation, emphasizing its potential in low-resource tasks and specialized applications like healthcare. Recent advancements in LLMs, such as GPT-3, LLaMA, and ChatGPT, have significantly enhanced the capabilities of generative AI, enabling the creation of realistic synthetic data. These models, trained on vast datasets, can generate coherent and contextually relevant text, making them valuable for data augmentation in various fields. Prompt engineering techniques, such as attribute-controlled prompts and verbalizers, are crucial for generating diverse and task-specific synthetic data. Parameter-efficient methods, including prefix tuning and LoRA, allow LLMs to be adapted for specific tasks without extensive retraining. The paper also addresses the challenges of synthetic data generation, including ensuring data quality, mitigating biases, and addressing ethical concerns. Synthetic data can be used to overcome the limitations of real data in low-resource and long-tail scenarios, enhancing model performance and enabling faster deployment. In healthcare, synthetic data has shown promise in tasks like medical imaging analysis and named entity recognition, where real data is scarce or sensitive. Despite these advancements, challenges such as hallucination, data privacy, and ethical implications remain significant. Future research should focus on improving data quality, developing robust ethical frameworks, and exploring new methods for synthetic data generation and application. The integration of LLMs in synthetic data generation not only enhances AI capabilities but also promotes responsible and inclusive AI development.