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

A method for generating synthetic data comprising:

• providing a prompt comprising a domain to a large language model (LLM);
• receiving, from the LLM, a data parameter associated with the domain that specifies a boundary for synthetic values in a column of data;
• providing an argument comprising the data parameter to a scalable data generation tool configured to generate data based on the data parameter;
• receiving, from the scalable data generation tool, scaled data comprising a column of synthetic data values, each synthetic data value within the boundary specified by the data parameter;
• and
• causing an emulated workload to utilize synthetic data comprising the scaled data to generate a performance benchmark for the domain.

A non-transitory computer-readable storage medium encoded with program instructions that, when executed by a processor circuit, perform a method comprising:

• providing a prompt comprising a domain to a large language model (LLM);
• receiving, from the LLM, a data parameter associated with the domain that specifies a boundary for synthetic values in a column of data;
• providing an argument comprising the data parameter to a scalable data generation tool configured to generate data based on the data parameter;
• receiving, from the scalable data generation tool, scaled data comprising a column of synthetic values, each synthetic data value within the boundary specified by the data parameter;
• and
• causing an emulated workload to utilize synthetic data comprising the scaled data to generate a performance benchmark for the domain.