Synthetic data generation utilizing generative artificial intelligence and scalable data generation tools

Abstract

Systems, methods, devices, and computer readable storage media described herein provide techniques for generating synthetic data utilizing generative artificial intelligence (AI) models and scalable data generation tools (SDGTs). A prompt comprising a domain is provided to an AI model. A parameter associated with the domain that specifies a boundary for synthetic values in a column of data is received from the AI model. An argument comprising the parameter is provided to an SDGT to generate scaled data based on the parameter. The scaled data comprises a column of synthetic values wherein each synthetic value is within the boundary specified by the parameter. In an aspect, an emulated workload is caused to utilize synthetic data comprising the scaled data to generate a performance benchmark. In a further aspect, a lightweight model is trained to generate synthetic sentences based on training data generated by the generative AI model based on the prompt.