The field of natural language processing and synthetic data generation is rapidly evolving, with a focus on developing innovative solutions to address data scarcity and improve model performance. Recent research has explored the use of large language models to generate synthetic data, including visual data for canine musculoskeletal diagnoses and tabular data for low-data regimes. These approaches have shown promising results, with improvements in model accuracy and reduced costs. Additionally, there is a growing interest in applying these techniques to specialized domains, such as maritime intelligence and medical text generation. Noteworthy papers in this area include: Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes, which proposes a framework for generating synthetic tabular data using a two-component approach. Leveraging Large Language Models to Effectively Generate Visual Data for Canine Musculoskeletal Diagnoses, which demonstrates the potential of large language models to generate synthetic visual data for medical diagnoses. Multi-Model Synthetic Training for Mission-Critical Small Language Models, which presents a novel approach for fine-tuning small language models using synthetic data generated by large language models.