Synthetic Data Generation Approach

Synthetic data generation (SDG) expands evaluation coverage by producing new rows or variants from a governed source. The output is an augmented dataset version with lineage to its seed.

Two situations call for it. When you already have a dataset but want more coverage or harder cases, augment it — the Image augmentation section perturbs drone imagery with a diffusion edit. When you have a document corpus but no evaluation set at all, generate one — the Q&A-pair generation section synthesises question–answer pairs for a RAG system.

Generated data goes through the same quality review and promotion process as uploaded data. Generation broadens the test set; it does not replace validation of labels or expected answers.

See the synthetic data tutorial.