You Did Not Train It. You Wrote It a Note.

Everybody Says They Trained It

Two people I know used the word "train" in exactly the same way. They each said they had trained an AI on their documents, or on a tone, or on a process. But they hadn't. What they had done was build a prompt. Sometimes they had even saved it in a file, but often not.

Why did they say they had trained it? Because everyone does. In this field, the word train means almost anything that can be done to an AI.

But the prompt they built is not the model. It survives only as long as that chat, or as long as that block of instructions. If you want to use it again, you have to copy it into the next tool. And if you close the tab, it is gone.

This is not because they are stupid. They are not stupid. It is because the industry uses the word "train" for everything.

Training Changes the Model

What is training? Training is changing the model's own numbers. It is adjusting the weights. The model behaves differently everywhere, permanently. Nothing you type in will do that.

Here is how it works. You show the model some text. It guesses what comes next. You compare its guess to what actually does come next, and nudge the weights so the next time it guesses slightly closer to the truth. You repeat this billions of times. What survives is not the text, but the adjustments.

The first time you do this, the model learns everything it knows unaided. It takes months, and enormous hardware, and a company-scale bill.

Training is the only way to change the model itself. Everything else is just telling it what to do with what it already knows.

What the model learns is a file of numbers. There is no document inside it. Nothing to look up.

Fine-Tuning Is the Small Version

The only other things that change the model are fine-tuning and low-rank adaptation. Fine-tuning is the same operation as the initial training, but starting from a model that is already trained, and using far fewer examples. Low-rank adaptation is even smaller: you freeze the original model and train a tiny layer beside it. It takes hours, not months; a single graphics card, not a cluster; and the result is a file you can email.

But both are still training. Both change the behaviour of the model with nothing typed in.

The thing about training is that it works best when the model is being nudged rather than filed. If you teach it to write in a certain style, or to format things a certain way, it will learn that well. If you try to teach it facts, it will learn them, but the wrong fact will be harder to remove.

If you train in a wrong fact, you can't just untrain it. You have to retrain it.

Prompting Changes Nothing

What almost everyone means when they say "I trained it" is prompting. A prompt is text that you put in front of the model when you ask it to do something. The model is unchanged before and after.

This includes the custom instructions you save in the settings of a chat app, or the custom assistants you build on a platform, or the "skill" files you hand someone. They are all prompts. The text is pasted in front of the model every time you ask it to do something.

Prompts are cheap and powerful. You can change a prompt in seconds, and undo it in seconds. The ceiling is that the text is re-sent every time, competing for room with everything else.

A prompt is a real skill. Just not training.

The Third Thing, and the One That Earns Its Keep

There is a third operation. It is deciding what the model is handed for this job. Which documents, which records, which history.

You store the material outside the model, and fetch what is relevant, and paste it beside the question. The model is unchanged. What changed is what it looks at.

This is what people mean when they say they "trained" their AI on their documents, or on a tone, or on a process. They are not changing the model. They are telling it what to look at.

This beats training for facts. If you want to fix a fact, you edit the file, and the model sees the right version the next time. You can even point at the source.

This is the thing that actually works when you say "trained on our documents." The model is not learning the documents. It is looking at them.

Closed Weights, Closed Door

Before you can say what you have done to the model, you have to answer a prior question: which model, and can anyone touch it?

The best-known models are closed. Their weights are unpublished. You cannot train them. You rent an interface to them, not a model. You send requests in, and text comes out.

Some vendors let you fine-tune their models for them. This is real training, on their terms, and the result stays theirs.

If you want to train one yourself, you need open weights. These are published as a downloadable file. Then it is yours, on your hardware, answering when the internet does not.

Knowing whether your model is open or closed tells you what you can do with it. If it is closed, you can only prompt it or do context engineering. If it is open, you can also train it.

What We Got, and What We Still Had to Hand It

We trained our own writing model. This is not a prompt. The model itself changed. A small trained layer rides on a much larger one. It loads beside it, and can be switched off.

We did it because we wanted something that would not sound like the stock model, and would not sound like AI. We bought voice: rhythm, sentence length, how it opens. No prompt came close.

What we did not buy was facts. The model knows nothing about us. Every article still starts as a brief, handed over at writing time.

This shows that training is not prompting, and that prompting is not context engineering. Training changes the model. Prompting changes what you tell it. Context engineering changes what it sees.

Which One You Actually Want

Most people who ask to train their AI on their documents do not want training. They want one of the other two, and they want it sooner and cheaper.

If you want it to sound like you, you need training. If you want it to know things about you, you need to hand it the things.

This is not a nitpick about vocabulary. Training, prompting and deciding what the model gets handed are three different operations with three different costs, three different failure modes, and — on the model most people are using — only one of them is even available.

Knowing which one you are doing tells you what you can expect it to have learned, and what it will forget the moment you close the tab.

Written by Oakleigh, Oak City Intelligence's writing agent, from an outline and a facts sheet by John. Reviewed before publication.