1 One token at a time
A language model writes by
- predicting54%
- guessing23%
- choosing14%
- copying9%
2 Model, product, agent
The short answerAn AI model is a computer program that learned from a huge number of examples. You give it a question, and it gives you an answer.
In simple words
- It learned from examples, not from rules someone wrote.
- Chat apps use a model to answer you.
- It writes by guessing the next word, again and again.
- It can sound sure and still be wrong.
Learning from examples
Most computer programs follow exact steps that a person wrote. An AI model is different. It learned by looking at a huge amount of text, pictures or code, a bit like learning a language by reading a giant library.
Nobody writes its answers in advance. It makes each answer new from what it learned.
Guessing the next word
When you ask a question, the model writes the answer one small piece at a time. Each time, it picks a piece that is likely to come next.
That is why its answers sound natural. It is also why it can be wrong and still sound confident: it picks likely words, and it does not check facts the way a person would.
The app around the model
ChatGPT, Gemini and Claude are apps built around models. The app adds the chat window, safety filters and extra tools.
An agent is a step further: it can also do things for you, such as open websites or fill in forms.
Check yourself
How does a model write an answer?
Does a model always check its facts?
What is ChatGPT?
0 of 3 answered
The short answerAn AI model is a program that learned patterns from huge amounts of data. It turns an input, such as a question, into an output, such as an answer, a picture or a decision.
In simple words
- A model learns from examples instead of following rules a programmer wrote.
- Language models such as GPT, Gemini and Claude learned from vast amounts of text.
- They write by predicting the next small piece of text, again and again.
- Apps and agents are built on top of a model; the model is the engine.
Learning instead of rules
Ordinary software follows steps that a programmer writes down. An AI model works differently: it is shown a huge number of examples and adjusts itself until its answers fit them.
What it learns is stored as numbers called parameters. Large models have billions of them. Nobody writes these numbers by hand; training sets them.
How a language model writes
Language models split text into tokens, small pieces of words. Given the tokens so far, the model predicts which token is likely to come next, adds it, and repeats.
This simple step, done thousands of times, produces answers, summaries and code. It also explains a known weakness: a model predicts likely words rather than looking up facts, so it can sound sure and still be wrong.
Model, product, agent
A model such as GPT-6 is the engine. A product such as ChatGPT wraps it with an app, safety filters and extra tools.
An agent goes one step further: it lets the model take actions, such as browsing, clicking or running code, to reach a goal. Many entries in our AI incident log involve agents.
How the law describes it
The OECD describes an AI system as a machine-based system that infers from its input how to produce outputs such as predictions, content, recommendations or decisions. The EU AI Act builds on the same idea.
The EU also has a separate term, the general-purpose AI model, for models that can do many different tasks. Their makers have their own duties, which the last lessons of this path cover.
Check yourself
How does a language model produce an answer?
What are a model’s parameters?
What is the difference between a model and an agent?
0 of 3 answered
The short answerA modern language model is a transformer network whose parameters were fitted to predict the next token. At use time it outputs a probability distribution over its vocabulary, and a decoding rule turns those probabilities into text.
In simple words
- Most language models use the transformer design from 2017, built around attention.
- Each step outputs probabilities for every token in the vocabulary.
- Decoding settings such as temperature decide how those probabilities become text.
- The EU AI Act calls these general-purpose AI models and gives their makers duties.
The transformer and attention
Most of today’s language models use the transformer, introduced in the 2017 paper “Attention Is All You Need”. Its key part, attention, lets every token weigh every other token in the context when building its representation.
Stacking many attention and feed-forward layers gives the model its capacity. Their weights are the parameters, which frontier labs often no longer disclose.
From probabilities to text
For each position, the model outputs a probability for every token in its vocabulary, typically tens of thousands to a few hundred thousand entries. A decoding rule then picks one token.
Greedy decoding always takes the most likely token. Sampling with a temperature adds controlled randomness: higher temperature gives more varied text, which is why the same prompt can return different answers.
Context and the system around the model
The context window is the most tokens the model can take into account at once, prompt and answer together. Longer contexts cost more computing.
Products wrap the model in system instructions, retrieval of documents, tool calls and safety classifiers. An agent runs a loop: plan, call a tool, read the result, repeat until the goal is met or a limit stops it.
Legal definitions
The EU AI Act defines a general-purpose AI model as one trained on large amounts of data, usually with self-supervision at scale. Such a model shows significant generality and can competently perform a wide range of distinct tasks.
That definition, not the brand name, decides whether a maker has the duties of Articles 53 to 55, covered in the training and frontier lessons.
Check yourself
What does attention let a transformer do?
What does a higher sampling temperature do?
Which EU term covers models like GPT, Gemini or Claude?
0 of 3 answered
Sources
- Updated definition of an AI system (OECD.AI)
- AI Act, Article 3: definitions (EU AI Act Service Desk)
- Attention Is All You Need (Vaswani and others, 2017, arXiv)
This lesson was generated by AI systems under the editorial rules of Silicon AI News and checked against the sources it lists. The live parts come from our checked stories, trackers, model comparison and rules checker.