When you generate synthetic training pairs using GPT-4o or Claude, you inherit those models' biases, knowledge cutoff limitations, and hallucination patterns. A synthetic SFT dataset generated by an LLM will systematically underrepresent topics where the generator has poor coverage, will replicate the generator's own reasoning biases, and may contain factually incorrect examples stated with high confidence — because LLMs generate plausible text, not verified truth.
Synthetic image data from diffusion models has different but equally serious problems: generated images often fail to represent edge cases found in real-world data (unusual lighting, partial occlusions, minority class appearances), the labeling of generated images by the same model that generated them creates circular quality issues, and the domain gap between synthetic and real data is often larger than expected even when synthetic images look realistic to human observers.
Our synthetic data QA service applies a multi-layer verification approach: statistical distribution analysis compares your synthetic dataset against real-world reference distributions; automated bias detection screens for demographic, topical, and factual biases introduced by the generator; and domain-expert human review validates a sampled subset for factual accuracy, label correctness, and real-world relevance. The output is a verified, documented synthetic dataset with a full quality report and data card.
What is distribution drift in synthetic data?
Distribution drift occurs when the synthetic dataset's statistical properties differ from the real-world data your model will encounter in production. An LLM generating synthetic customer service conversations will overrepresent polite, well-formed requests (because it was RLHF-trained to prefer these) and underrepresent angry, grammatically poor, or code-switching requests that real users frequently send. A model trained on this distribution will perform worse on real users than on the synthetic benchmark.
Can we also generate synthetic data for you?
Yes. We can generate synthetic training data using LLMs (with careful prompt engineering for diversity and edge case coverage) and then run it through our expert verification layer. This gives you the cost efficiency of synthetic generation with the accuracy guarantee of human verification. We do not deliver synthetic data without human verification — the two steps are always combined in our service.
What is a data card and why do you need one?
A data card is a standardised documentation artifact describing your dataset: how it was created, what it represents, who verified it, what biases are known, what limitations apply, and what use cases it is appropriate for. For synthetic datasets specifically, the data card must document the generator model used, the generation prompts and parameters, the verification methodology, and the known gaps. Without a data card, your synthetic dataset is an undocumented risk in your training pipeline.
