Data → Weights → Predictions

When someone says "we trained a model," the mind reaches for something almost alive - a little brain that read a library and woke up clever. That picture is why training feels mysterious. Let's replace it with one that's accurate and far less spooky.

Here's the secret this phase delivers: a model is a big pile of numbers, and those numbers are the only thing training ever changes. Once you see that, everything else in this guide is detail.

What a model actually is

What it actually is. A model is a fixed recipe with a lot of adjustable knobs. The recipe says how to turn an input into an output - multiply this, add that, combine these. The knobs are numbers, and they decide what the recipe actually produces. Those numbers have a name.

📝 Terminology. A weight is one of those adjustable numbers inside a model. A real model can have anywhere from a handful of weights to billions of them. When people say a model has "7 billion parameters," they mean it has roughly that many of these knobs. Weight and parameter are used almost interchangeably.

Why people get this wrong. The common picture is that the model stores the data it was trained on, like a database you can search. It doesn't. After training, the original examples are gone; what remains is the settings of the knobs - the weights that the examples produced. The data shaped the numbers and then left the room.

What it does in real life. A trained model is a file full of numbers. You hand it a new input, it runs that input through the recipe using its current weights, and out comes a prediction. Same recipe every time; the weights are what make one model good at spotting spam and another good at finishing your sentences.

So what is a "prediction"?

What it actually is. A prediction is the recipe's output for an input it may never have seen before. "Prediction" sounds like fortune-telling, but in machine learning it means any answer the model produces: a label ("this email is spam"), a number ("this house is worth $420,000"), or the next word in a sentence.

A real example. Imagine the simplest possible model: one that guesses a house's price from its size. The recipe is "price = size × weight + another weight," and training's whole job is to find good values for those two weights.

   Before training (random weights):
     1,500 sq ft  ──►  model guesses  $38,000     (wildly wrong)

   After training (weights tuned on real sales):
     1,500 sq ft  ──►  model guesses  $410,000    (close to reality)

What just happened: Nothing about the recipe changed between those two lines - it's the same "size × weight" formula both times. Only the two numbers inside it moved. Training looked at real houses with known prices and slid those numbers until the formula's guesses started matching reality. That sliding is learning.

The gotcha. ⚠️ A model can only predict things shaped like what it was trained on. The house model knows nothing about cars, and feeding it a car's data won't get you a sensible answer - it'll confidently return a number anyway, because the recipe always produces something. A model never says "I don't know" unless it was specifically built to; by default it always answers, even when the question is nonsense. Hold onto that - it explains a lot of strange AI behavior later.

Why this saves you later. Once "a model is tuned numbers running a fixed recipe" is your mental picture, the scary words deflate. "Loading the model" means loading those numbers. "The model is 4 GB" means the numbers take up 4 GB. "Fine-tuning" means nudging numbers that were already mostly set. You can reason about all of it instead of treating it as a black box.

Recap

1. A model is a fixed recipe plus a large set of adjustable numbers called weights (a.k.a. parameters).
2. Training only ever changes those weights - it does not store the original data inside the model.
3. A prediction is the recipe's output for a given input, using the current weights.
4. A model always produces some answer, even for inputs it has no business answering - being right is what training is for.

Now we know what training changes. Next: how it figures out which way to nudge each number.

Watch it animated: training vs. inference

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