training data
Training data is the text, images, audio or other examples from which an artificial intelligence model learns patterns before you start using it.
In plain terms
Artificial intelligence is not built by someone writing down rules step by step on what to do. It is shown countless examples, from which it derives the patterns itself. These examples are the training data.
It is like a child learning to speak: no one explains the grammar, yet they grasp it because they hear those around them for years. If people speak with a dialect at home, the child will too. The model similarly reflects what it saw in its examples, including errors and biases.
Therefore, the training data determines an AI system's knowledge at least as much as the program itself. The model knows little or nothing about topics for which there were no examples in it. What appeared distorted in it, it returns distorted.
Important distinction: training data is the model's 'school curriculum,' processed during its creation. What you type or upload during a conversation is not training data but temporary working material. Whether it is later used for training depends on the provider's rules and your settings.
How it works
- Collection: developers gather examples, such as websites, books, images, audio recordings, program code, or a company's own data.
- Cleaning and labeling: duplicate, erroneous, or harmful content is filtered out, and in many cases humans mark what is visible or what the correct answer is for a given example.
- Lesson: the model goes through the examples many times, guesses, its guess is compared with the correct result, and its internal settings (parameters) are gradually adjusted so that it makes smaller mistakes next time.
- Verification: the model is tested on data it did not see during training, because this reveals whether it truly learned the patterns or just memorized the examples.
- Cutoff: training ends with data collected up to a specific point in time, so the finished model does not know about later events on its own, unless it receives a search tool or updated document.
An everyday example
You ask a chatbot about last week's news, and it confidently recounts an earlier state or even makes something up. It is not lying intentionally: its training data closed at an earlier point, and it only knows what happened after that if it searches the internet in the meantime.
Who is affected
A common misconception
Limits and risks
- The model adopts the biases and errors in the training data, so its response can be one-sided or incorrect even if it sounds confident.
- Training data is frozen at a specific point in time, so the model does not know about recent events, prices, or regulations on its own.
- Hungarian-language and Hungary-related content is far less abundant in training data than English, so models make more mistakes on domestic topics.
- From the outside, it is rarely possible to know exactly what a model was trained on, so check the tool's data-handling settings before entering sensitive data.
Similar, but not the same
- tesztadat – Test data is intentionally withheld from the model during training so that its ability to handle unknown examples can be measured at the end.
- prompt – The prompt is the request you give to the pre-trained model during use, and by itself it does not change the model's knowledge.
- knowledge base – The knowledge base allows the AI system to retrieve fresh or company-specific information when answering and passes it to the model. Training data, by contrast, is built into the model's operation during its creation.
Related concepts
labeled data, synthetic data
tesztadat, validation data, model training, fine-tuning, bias, knowledge cutoff, data protection
A brief history
The term became a basic concept alongside machine learning. The expression 'machine learning' was popularized by Arthur Samuel in 1959 with his study on a program learning to play checkers. The importance of large, carefully labeled data collections was demonstrated by the ImageNet image database introduced in 2009. This became truly evident in 2012, when a neural network trained on it (AlexNet) achieved a spectacular breakthrough in image recognition.
Where things stand
Every modern AI system is built on training data, so the concept is common in both the profession and public discourse. At the end of 2026, the debates focus mainly on copyright, licensing, and transparency. Since August 2025, the EU AI Act has required providers of general-purpose models to publish a public summary of the content used for training.
In the news
- LG AI Research tool checks copyright risk in training data 45 times fasterNov 4, 2026 · Jog
- Thomson Reuters v. ROSS: Third Circuit recognizes licensing market for AI training dataOct 27, 2026 · Jog
- Factory AI's valuation triples in five months to $5 billionOct 12, 2026 · Code
- ANI v. OpenAI: Indian analysis of AI training and fair dealingOct 6, 2026 · Jog
- California enacts more than two dozen privacy and AI laws in 2026 sessionOct 5, 2026 · Jog
- IFR: about 7,000 humanoid robots sold in 2025, mostly for research and data collectionSep 21, 2026 · Ipar
- US government sides with OpenAI in copyright dispute as Zuckerberg opposes national AI regulatorSep 2, 2026 · Agent Age
- Agile Robots brings physical AI to factories and collects robot training dataSep 1, 2026 · Ipar
Also known as
training data · training data · training data · training data · training data · training dataset · training data · training dataset
Sources
This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.