Technique Updated 2026-04
Synthetic Data
Definition
Synthetic data is data artificially generated by algorithms or AI models, designed to reproduce the statistical properties of real data without containing personal information.
See also in the glossary
G
Generative AI
Generative AI refers to artificial intelligence systems capable of creating original content: text, images, video, audio, code.
F
Fine-tuning
Fine-tuning is the process of retraining an existing AI model on a specific dataset to adapt it to a particular domain or task.
M
Machine Learning
Machine Learning is a branch of AI where systems learn from data to improve their performance without being explicitly programmed for each task.
D
Deep Learning
Deep Learning is a subset of Machine Learning using multi-layered neural networks to learn complex representations from raw data.
L
LLM (Large Language Model)
An LLM is an AI model trained on billions of texts, capable of understanding and generating human language.
R
RLHF (Reinforcement Learning from Human Feedback)
RLHF is a training technique that uses human feedback to align an LLM's behavior with user expectations.
Tools that use synthetic data
Frequently Asked Questions
Can synthetic data replace real data?
Not entirely. Synthetic data is a powerful complement to real data: it fills gaps, increases diversity and respects privacy. But a model trained solely on synthetic data risks model collapse — grounding in reality is always needed.
How is synthetic data generated?
Several methods exist: LLMs like ChatGPT or Claude for structured text, GANs for images, diffusion models, physics simulators, and classic statistical techniques like SMOTE for tabular data.
