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Written Explanation

The assistant is a model plus an interface

When you type into an assistant, you talk to a program that sends your text to a large language model, or LLM. The LLM is the part that produces the answer. Everything else (the chat window, the editor plug-in) is packaging.

An LLM is a mathematical model with a very large number of adjustable numbers inside it. It is trained on huge collections of text and code. During training it practises one task over and over: given some text, guess what comes next. Its internal numbers are adjusted until its guesses become good. After training, you use the model to continue your text.

Tokens

The model does not read letters or whole sentences. It reads tokens: small pieces of text. A token can be a whole word, part of a word, a single character, or a piece of punctuation. Google's machine-learning course describes tokens as the smallest unit a language model works with, and gives a rough rule for English of about four characters per token. Exactly how text is split depends on the model, so the splits below are only an illustration.

Text:    The list is empty.
Tokens:  The | list | is | empty | .

Code:    names.add("Ana");
Tokens:  names | . | add | (" | Ana | "); 

Notice that code is tokenized too. To the model, Java is just another kind of text with strong patterns: after names. a method name usually follows, after ( an argument usually follows.

Next-token prediction

Here is the whole generation loop:

1. Your question is split into tokens.
2. The model computes, for every token it knows, how likely it is to come next.
3. One token is chosen (often a likely one, sometimes with a little randomness).
4. That token is added to the text, and the loop repeats from step 2.

The answer therefore grows left to right, one token at a time. There is no separate step where the model plans the full answer, looks in a reference book, or tests it. Google's course calls such models, in effect, autocomplete mechanisms that can complete very long stretches of text.

Training data and patterns

What the model "knows" is whatever patterns were useful for guessing the next token in its training data. A model that saw millions of Java examples learns that public static void main(String[] args) is a very likely continuation of public class Main {. It also learns style, common mistakes, and typical explanations.

Two consequences follow:

• Quality depends on the data. Patterns that were common in training are reproduced well. Rare things are reproduced badly.
• The data stops at some date. Anything newer than the training cutoff was never seen. The next lesson returns to this.

A toy version of the idea fits in a few lines. In the examples you will build a tiny model that counts which word follows which, then generates text from the counts. A real LLM is vastly more sophisticated, but the job is the same: continue the text with a likely next piece.

What the model does not do by itself

• It does not run your code. It never compiled your program. When it says "this prints 6", that is a prediction of what such an answer looks like.
• It does not look things up in the documentation while answering, unless the product has a search or documentation tool attached. When such a tool exists, its results are still text that you should check.
• It does not remember your project beyond what is in the conversation or what the tool sends it.
• It does not know when it is guessing. Producing a likely token feels the same, to the model, whether the result happens to be true or not.

Fluent is not the same as correct

Because the model is trained to produce likely text, its output reads smoothly. Smooth reading is a different property from being true. A correct answer and an invented one can have the same tone, the same formatting, and the same confidence. The compiler, a test, the debugger, and the official documentation are the judges, not the assistant's tone.