What is AI training data? A guide for businesses

What actually is training data? The term appears in almost every article about AI, but is rarely explained. In short, AI training data is the material an AI model learns from. Just as a person learns a language by reading and listening, a language model learns by processing huge amounts of text. This guide covers the basics – what training data is, what kinds exist, where it comes from and why it has become an issue for ordinary European businesses too. At the bottom there is a glossary of the most common terms.

What is training data – the short explanation

Training data is a collection of examples. It can be:

• text – articles, books, emails, chats, manuals,
• images – photos, illustrations, scanned documents,
• audio – calls, podcasts, recordings,
• video – films and clips,
• code – source code from software projects,
• structured data – tables and databases.

An AI model analyzes the examples and finds patterns in them. If the model has seen enough examples of how sentences in a language are built, it can write that language itself. If it has seen enough pictures of dogs, it can recognize a dog. The model doesn't store the material like an archive; instead it adjusts its internal settings so it gets better at predicting and creating similar content.

How an AI model learns – a simple analogy

Picture a new hire in customer service. In their first week they read old cases: what customers asked, what colleagues replied and which answers solved the problem. After a while they start to recognize typical cases and know how they are usually handled.

An AI model does something similar, but at a much larger scale and in a statistical way. The more and better examples it gets, the better it becomes. And just as for the new hire, the quality of the examples matters – bad role models create bad habits.

Different types of training data

Training happens in several stages, and each stage has its own data needs.

Pre-training

In pre-training, the model learns language and general knowledge by reading enormous amounts of text. Breadth and volume matter most here. Much of the material comes from the open web, but also from books, newspapers and other licensed sources.

Fine-tuning

After pre-training, the model is adapted for specific tasks, such as following instructions or answering politely and correctly. This needs less, but more carefully selected, data – often examples of questions and good answers.

Agent training

More and more AI models are trained to carry out multi-step tasks – so-called AI agents. They need data that shows how real work gets done: how a case is handled, how a decision is made, how a follow-up is done. That is why AI labs are increasingly looking for workplace data such as Slack, email and support tickets.

Evaluation data

To measure how good a model is, you need test material the model hasn't been trained on. Such data is used to compare models and spot weaknesses.

Where does training data come from?

There are three main sources:

1. Openly available material – mainly text from the web. It is vast but uneven in quality, and the largest models have already been trained on most of it.
2. Licensed material – data bought or licensed from rights holders, such as publishers, media archives or companies.
3. Synthetic data – material generated by other AI models. It can fill gaps but doesn't fully replace real examples.

Smaller European languages are underrepresented

For Nordic and many other European languages, the shortage of good training data is clear. AI Sweden built GPT-SW3 on a Nordic corpus, and OpenEuroLLM has had to generate synthetic data because there isn't enough real text. In Norway, the National Library saw that licensed books and newspapers improved Norwegian models. In Sweden, the government has appointed a national coordinator for Swedish language models, who is also looking at compensation for rights holders.

Rules that govern training data

There are three sets of rules that are particularly important to know. What follows is general information, not legal advice.

Copyright. Under the EU's Digital Single Market (DSM) Directive, as implemented in each member state, text and data mining is permitted unless the rights holder has reserved their rights against it in an appropriate way. Such a reservation, often called an opt-out, combined with a license, is how a rights holder can move from their material being used for free to it being used for payment.

The EU AI Act. Under Article 53, providers of general-purpose AI models must respect such reservations and publish a summary of the content they have trained on, using a template from the European Commission.

GDPR. If the data contains personal data, a lawful basis is required, and the use must be compatible with why the data was collected. Health data and other special categories require particular care. How rights and terms are handled in an actual deal is described under data licensing.

What makes training data good?

Not all data is equally valuable. What usually matters:

• Quality – accurate, well-written and without a lot of noise.
• Uniqueness – material that isn't already everywhere on the web.
• Language – Nordic and other smaller languages are sought after.
• Context – complete sequences and reasoning rather than isolated fragments.
• Clear rights – it must be possible to show where the data comes from and that it may be used.
• Documentation – metadata describing what the data contains.

Common misconceptions about training data

"More data is always better." Volume matters, but only up to a point. Duplicates, noise and errors can make a model worse. A smaller, high-quality dataset can be worth more than a large, messy one.

"The AI model stores everything it reads." A model generally doesn't store the material like an archive; it learns patterns. That doesn't mean the risk of personal data or sensitive content leaking is zero, which is why de-identification matters.

"Everything on the internet is free to use." Being published doesn't mean something lacks copyright. Rights holders can reserve their rights against text and data mining.

"Synthetic data solves the shortage." Synthetic data can help, but it is built on what another model already knows. Real examples are still needed for models to learn anything new.

Glossary

AI agent – An AI system that can carry out multi-step tasks, for example reading a case, fetching information and sending a reply.

Anonymization – Processing data so that no person can be identified any longer, directly or indirectly. The threshold is high, especially in free text.

Annotation – Labeling data, for example stating what an image shows or whether an answer is good or bad.

Corpus – A structured collection of texts or other material. Read more on what is a corpus.

Data license – An agreement that gives someone the right to use data on defined terms, for example for a certain period and a certain purpose, without ownership being transferred.

Fine-tuning – A training stage where an existing model is adapted for specific tasks using smaller, selected data.

GPAI – General-purpose AI models. The term is used in the EU AI Act.

Language model – An AI model trained on text to be able to understand and produce language.

Opt-out (reservation) – When a rights holder states that their material may not be used for text and data mining without permission.

Pre-training – The first, large training stage where the model learns language and general knowledge from large amounts of text.

Pseudonymization – Replacing names and identifiers with codes. The data still counts as personal data.

Reinforcement learning (RL) – A training method where the model tries to solve tasks itself and is rewarded when it succeeds.

Synthetic data – Data created by an AI model rather than by people. Creating synthetic data from personal data is itself processing of personal data.

Text and data mining (TDM) – Automated analysis of text and data, for example to train AI. Governed in the EU by the DSM Directive.

Training data – The material an AI model learns from.

Training content summary – The summary of training content that providers of general-purpose AI models must publish under Article 53 of the AI Act.

What does this mean for your company?

Many companies have more training data than they think. Published texts, manuals, image archives, audio recordings and source code are often the simplest route, because the rights are clear and there is little personal data. Operational data such as email, chats and support tickets can be in high demand, but always requires a careful assessment of personal data and rights.

If you want to know how a license works in practice, and which data types AI companies are looking for right now, there is a more business-focused overview on our page about AI training data.