Synthetic data is artificially generated information — text, images, tabular records, sensor readings — produced by algorithms, simulations, or generative models rather than collected from the real world. It is used to augment scarce real datasets, preserve privacy by replacing sensitive records with statistically equivalent substitutes, and create the controlled edge-case distributions that real-world collection rarely delivers. Quality control is paramount: if the generative process encodes errors or bias, those flaws propagate into every downstream model trained on the output, a phenomenon sometimes called model collapse.

Think of it like a film studio building a photo-realistic miniature set instead of shooting on location. The miniature can stand in for scenes too dangerous, too expensive, or legally off-limits to film for real — provided the model-makers capture the right details; a poorly built set fools no one.

from sklearn.datasets import make_classification
X, y = make_classification(n_samples=10_000, n_features=20,
                            n_informative=5, random_state=42)
# X and y are fully synthetic; no real user records were used

In the LLM world, models like GPT-4 are routinely prompted to produce synthetic question-answer pairs for Fine-tuning smaller models, enabling Knowledge Distillation at scale without labelling cost.

Synthetic data intersects closely with GANs and Diffusion models, which are the most powerful engines for generating realistic synthetic images and audio; it also feeds into RLHF pipelines where human preference labels are expensive to collect at the required volume. The boundary between synthetic pre-training corpora and Pretraining on real web data is increasingly blurred in modern Foundation Model development.

You will encounter synthetic data when sourcing training sets for regulated domains (healthcare, finance), when bootstrapping a new product with no historical data, or when reading research papers that compare model performance across real versus generated datasets.