Glossary

Training Data

What is 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.

How KI.ordo helps

According to Anthropic's commercial terms of service (as of October 2026), Anthropic may not train models on customer content from its services, and the 👍/👎 ratings in KI.ordo are used only to improve knowledge and instructions, not to train models.

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Topics

Data
Training
Data protection

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Related terms

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.

test data
data protection
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Template

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.

training data
data quality
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Garbage in, garbage out (GIGO)

‘Garbage in, garbage out’ describes the principle that a system can only produce results as good as the data it receives. This applies doubly to AI: incorrect, outdated or one-sided training or knowledge data leads to wrong or biased answers. A chatbot that draws on an outdated price list will quote outdated prices. Looking after data is therefore an ongoing task in every AI project.

Data quality
Principle
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Bias

In AI, bias refers to a systematic distortion of results. It often stems from unbalanced or historically skewed training data, but also from model design or how a question is framed. A well-known example: an applicant screening tool that learns from past hiring decisions can perpetuate existing disadvantages. In statistics, bias also means the deviation of an estimate from the true value. Countermeasures include diverse data, regular audits and human decisions on personnel matters.

AI ethics
Fairness
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