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Glossary

Synthetic data

What is Synthetic data?

Synthetic data is generated artificially rather than collected in the real world, but mimics the patterns and properties of real data. It helps when genuine data is scarce, expensive or sensitive from a data protection perspective – for example for testing software, training models or checking a chatbot with realistic sample queries. It is important that it reflects reality closely enough and does not allow conclusions about real people.

How KI.ordo helps

The KI.ordo template “Generate test questions” creates 20 realistic test queries without real personal data from a product or chatbot description.

Learn more →: Synthetic data

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Topics

  • test data
  • data protection
  • training data

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Related terms

Training Data

Training data are the examples from which an AI model learns patterns: large text collections for language models, labelled images for image models. Their quality, diversity and recency largely determine how reliable and fair a model is and how up to date its knowledge will be. For organisations it also matters whether a provider uses the customer data they enter for further training.

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Data scarcity

Data scarcity occurs when there is too little suitable or labelled data for an AI task to train or test a model reliably. It affects, for example, rare defect types in production, niche languages or new products without a sales history. Remedies include transfer learning, synthetic data, data augmentation or pre-trained language models that deliver useful results from just a few examples.

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Data protection regulations

Data protection regulations are legal rules governing the handling of personal data. In the EU the General Data Protection Regulation (GDPR) is the key framework; among other things it requires a legal basis for every processing activity, data minimisation, transparency towards data subjects and data processing agreements with service providers. When using AI, organisations need to clarify which data ends up in inputs, knowledge sources and logs, and where it is processed.

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Generative AI

Generative AI refers to AI systems that create new content – text, images, audio, video or program code – rather than merely sorting or assessing existing data. They are based on models that learn patterns from large amounts of data and use them to produce plausible new outputs. In business it is used, for example, to draft emails, summaries, product descriptions or image ideas. Results can contain errors and should be checked before use.

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