Training & Fine-tuning

Synthetic Data

AI-generated training data used when real data is scarce, sensitive, or expensive to label.

Definition

Synthetic data is artificially generated content used to train or test AI models. It can be created by AI systems generating plausible examples, or by mathematically augmenting existing data. Synthetic data is valuable when real data is hard to obtain (rare events), legally restricted (medical records, financial data), or expensive to label (requiring expert annotation). Modern LLMs themselves are increasingly used to generate training data for specialised models.

Why this matters for your business

Synthetic data generation makes it possible to build custom AI models without exposing real customer data, which is important for GDPR compliance and information security.

Related Terms

Training Data

The text, documents, or other content an AI learns from during its development.

Fine-tuning

Further training a pre-trained model on specific data to specialise it for a task.

Data Augmentation

Artificially expanding training data by creating variations of existing examples.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Training & Fine-tuning·Synthetic Data