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JEcomment “AI” for my full synthetic data tutorial Youtube video! save for later & follow for more!
You can customize any dataset for any industry, business problem, or project and get way more interesting data than Kaggle.
Plus, you can ask for imperfect data with inconsistent values, duplicates, or nulls to make it feel more realistic to the real world. You just have to know how to specify your requirements and constraints when prompt engineering.
Here’s what you should specify:
✨ size of dataset(s) (rows / columns)
✨ column names and data types
✨ primary keys and foreign keys
✨ distribution and allowed values
✨ variation of datapoints
✨ downloadable as CSVs
✨ anything else that may impact your project!
Full example below:
You are a data engineer generating a realistic synthetic dataset for [INDUSTRY] and [PROJECT TYPE OR PURPOSE].Can you generate [NUMBER] realistic datasets with the following requirements.Create an [TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns you think would be useful. [PRIMARY KEY] is the primary key. [FOREIGN KEY 1] and [FOREIGN KEY 2] are foreign keys that connect to the [RELATED TABLE NAME] table. Ensure that [NUMBER] foreign key values exist in the related table but do not appear in this table (to simulate missing relationships).Create a [DIMENSION TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns. [PRIMARY KEY] is the primary key and connects to the first table. Ensure that [NUMBER] records in this table have no matching rows in the first table.For both tables, include high variation across values, non-even category distributions, and realistic data patterns. All ID fields should be random numeric values only (no letters).[Add in any other requirements, constraints, or behavior rules]Return each table as a separate, downloadable CSV file.
Have you tried this hack and said goodbye to Kaggle yet?
@jessramosdata
