Using an open-source implementation of GPT-3, this text discusses the process of augmenting machine learning datasets with synthetically generated text and labels, offering a scalable, fast, and cost-effective alternative to traditional data augmentation methods. The approach involves fine-tuning a GPT model on a financial intent classification dataset named `banking77`, which contains 13,083 customer service queries labeled with 77 intents. By encoding intent labels and text into a single field and using conditional generation, the model generates new annotated examples for the intent classes. The text highlights the advantages of synthetic data, such as improved privacy and reduced costs compared to real-world data collection and annotation, and cites a Gartner prediction that synthetic data will dominate AI data use by 2030. The process employs GPT-Neo via Gretel.ai’s APIs, but other GPT models from the HuggingFace repository can also be used. The synthetic data is generated by seeding the model with class examples, and the output is formatted into a tabular format for analysis. The text concludes by expressing enthusiasm for the capabilities of generative pre-trained transformers and invites readers to explore further resources and participate in the community.