training

basicwidespread

Training is the process in which an AI model learns from countless examples, through gradual attempts and corrections, how to solve a task.

In plain terms

Traditional software is written rule by rule. An AI model is not written but trained: many examples are shown to it, and it identifies patterns on its own.

It is like when a child learns to ride a bicycle. They do not memorize physics formulas but wobble thousands of times, correct, and eventually get a feel for balance. The model guesses the same way, learns how big the error was, and adjusts itself slightly.

At the end of training, a finished model is produced whose "knowledge" is stored in a large number of configured values (parameters). In a large language model, there can be several billion of them. When you chat with a chatbot, you are using this finished model, and it does not keep learning during the conversation. Some services do remember things about you (a "memory" feature), but they do so by saving separate notes, not by retraining the model.

Training large models is one of the most expensive parts of AI: it can take weeks or months, requiring vast amounts of data, computing power, and electricity. Therefore, only a few companies can build the largest models.

How it works

Data collection: they compile the training material, i.e., texts, images, audio recordings, or tables. These are often labeled (for example, 'there is a cat in this image'), but with language models, the raw text alone is sufficient.
Guessing: the model receives an example and attempts to provide the answer, for instance, by guessing the next word in a sentence. At the start, its internal numbers are random, so it almost always makes mistakes.
Error measurement: a formula calculates how much the guess deviated from the correct answer. This number is the error (loss in English).
Correction: the system calculates backward from the error how much to adjust which internal number (weight) to reduce the mistake next time.
Repetition and verification: this loop runs countless times, even millions of times for large models. During and at the end, the model is tested on examples it has not seen before. This reveals whether it truly learned or merely memorized.

An everyday example

Your email spam filter recognizes unsolicited messages because it was trained on millions of emails previously labeled as 'spam' or 'not spam'. When you mark an email as spam yourself, you typically provide a new training example for a future training round.

Who is affected

everyoneWhat you publish online or type into an AI service can become training data for a model, depending on the settings.

entrepreneur, executiveYou must know whether the provider trains a model with your company's data, and whether you need your own training or if using a pre-trained model is sufficient.

developerA model's capabilities and errors are determined by the training data and the training method, so you must understand this before building an application or agent on it. → Code

lawyer, citizenMost copyright and data privacy disputes concern which works and personal data may be used to train models. → Law

A common misconception

MythThe model learns immediately from every conversation, so what you tell it today, it will know for others tomorrow.

In realityTraining is a separate, closed phase; the finished model does not change during use. At most, your conversations may end up in the training material of a later training run, if the provider's rules allow it. In many places you can switch this off. The "memory" feature only saves data to your own account; other users cannot see it.

Limits and risks

The model only knows what was included in its training data: it has no inherent knowledge of events after training ended, unless provided with a search tool or updated documents.
If the training data is incorrect, one-sided, or biased, the model learns this too and confidently passes it on.
Training is not exact storage: the model records patterns, not facts, so it may give plausible but false answers.
Training large models consumes vast amounts of electricity, water, and money, and the origin of the training data is often opaque.

Similar, but not the same

inference – Training is the learning phase, while inference is when the finished model answers your question and no longer changes.
fine-tuning – Fine-tuning is the shorter, targeted further training of an already trained model for a narrower task, not training from scratch.
Prompting – A prompt guides the model only within that conversation and does not overwrite its internal parameters, whereas training shapes the model itself.

Related concepts

Broader

machine learning, artificial intelligence

Narrower

pre-training, fine-tuning, supervised learning, reinforcement learning, self-supervised learning

On the edge

training data, inference, overfitting, neural network, model weights, deep learning

A brief history

Frank Rosenblatt introduced the perceptron in 1958, one of the first machines that learned from examples. Backpropagation, the basis of modern training, was already described in the 1970s (Seppo Linnainmaa, Paul Werbos). It was widely popularized by David Rumelhart, Geoffrey Hinton, and Ronald Williams in their 1986 study.

Where things stand

By the end of 2026, training remains the foundation of today's machine learning-based AI systems: large language models are trained in massive data centers at enormous cost. Since August 2025, the EU AI Act has required providers of general-purpose models to publish a summary of the content used for training.

Status: 2026 November

In the news

Appears in 193 Agent Age stories; the curve shows weekly occurrence in the news magazine.

Cainiao opens its global warehouse network for training embodied AINov 5, 2026 · Ipar
New standard proposal would simplify how publishers opt out of AI trainingNov 4, 2026 · Marketing
LG AI Research tool checks copyright risk in training data 45 times fasterNov 4, 2026 · Jog
South Korea launches AI data factory with 12 bipedal humanoid robotsNov 3, 2026 · Ipar
Fear, lack of training and limited decision-making power slow workplace AI adoption in HungaryNov 2, 2026 · Work & HR
White-collar workers quit careers over AI fears or seek 'AI-proof' jobsNov 2, 2026 · Work & HR
Getting employees to embrace AI takes more than training, reports sayNov 2, 2026 · Work & HR
Samsung: 95% of students use AI, but few get formal training; parents struggle to adviseNov 2, 2026 · Education & Family

Also known as

training · training · model training · training · training · training · model training · AI training

Sources

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.