An AI model trainer is the person who shapes how an AI model behaves, using data and human feedback. Training passes through three layers. First the model reads an enormous amount of text and learns patterns, then it is steered with examples, and finally people score and correct its outputs. I have done this work myself. I trained models, audited their outputs, and corrected their Turkish. On this page I explain that process in plain language, without crossing any confidentiality lines.
Pick any AI you can think of. At some point, somewhere, someone trained it. Most people never think about that. They open the app, ask a question, get an answer and say "so that's how smart it is." But behind that answer sits months of training. And inside that training, somewhere the screen never shows, are people who read every answer the model gives, one by one, and score it and correct it.
I was one of those people. I trained models, audited the answers they produced, and watched up close how an AI learns to tell "right" from "wrong." On this page I'll tell you two things. First, how an AI model is really trained, in plain language, no technical background needed. Second, what I saw with my own eyes doing this work. Because reading about something and doing it with your own hands are not the same thing. Once you do the training yourself, your view of AI changes completely. That changed view is what I want to pass on to you.
What is an AI model trainer?
Let's name it clearly first. An AI model trainer is the person who shapes how an AI model behaves. This is not the engineer who codes the model from scratch. It's the person who steers the model and says "this is a good answer, that is a bad one."
Think of it this way. A model learns from millions of examples, but it can't tell on its own which example is good and which is bad. Someone has to teach it that. That is the model trainer's job. Sometimes you write sample answers for the model. Sometimes you put two answers the model produced side by side and say "this one is better." And sometimes you audit an answer from top to bottom and mark where it went wrong.
In Turkish, this job doesn't have a settled name yet. I call it Yapay Zeka Model Eğitmeni, literally AI Model Trainer. Most people in the field just use the English term, but what matters isn't the word. It's what the job actually is. The person who decides an AI's character, its tone, and what it will and won't do is the person doing this work. In other words, the AI model trainer.
A model is trained in three stages
The cleanest way to explain how an AI model is trained is to split it into three layers. Once you grasp these layers, you start to see what sits behind every tool you use.
The first layer is pretraining. Here the model reads an enormous amount of text. Books, articles, writing from across the internet. But don't picture "reading" the way a person reads. The model learns only one thing from all that text. Which word is most likely to follow another in a sentence. By repeating this billions of times, it absorbs the patterns of language. The result is a raw model that uses language fluently but has no direction and no manners yet.
The second layer is steering. Here the model is given examples. "When this question is asked, the answer should look like this." Across thousands of examples, the model learns not just to predict words but to be helpful, to answer the question, to follow a format. This is where the raw model starts turning into an assistant.
The third layer is human feedback. This is the most human part of the work, and the part I was most deeply involved in. The model produces several different answers to one question. People read those answers, compare them, score them. "This one is more accurate, this one is more polite, this one is safer." When the model learns from these human preferences, we call it, in plain terms, learning from human feedback. The technical name is RLHF, reinforcement learning from human feedback. The model's "character" is largely shaped here.
Data is the foundation of everything
Let me give you the clearest lesson I took from inside this work. A model is only as good as the data it was trained on. Not an ounce more, not an ounce less.
If the data is bad, the model will be bad. If the data is biased, the model will be biased. If the data is full of one particular point of view, the model will take that point of view for the whole world. Read from the outside, this sounds like a slogan. Lived from the inside, it feels like a responsibility. Because whatever you show the model, it learns, and then it hands what it learned back to millions of people.
That's why the most boring-looking part of training is actually the most critical. Cleaning the data, sorting it, balancing it. Which example gets in, which one gets cut. It isn't a shiny model that makes the difference. It's clean data. Anyone who has seen this asks one question first about any AI tool. "What was this fed on?"
Human feedback shapes the model's manners
This is the least understood and most decisive part of training, so let me unpack it a little. In its raw state a model looks smart but can be crude. It can drift in the wrong direction, make things up where it isn't sure, blindly go along with a harmful request. What teaches it manners is human feedback.
So how does it work? The model produces two, three, sometimes more answers to one question. A person sits down, reads these answers and ranks them. Which one is more accurate, which one is clearer, which one is safer. The ranking goes back to the model, which adjusts itself as if to say "so this is what people prefer." This is repeated thousands, hundreds of thousands of times. Slowly, the model drifts toward the answers people find good.
Doing this work, I noticed something. A model that comes across as polite, helpful and balanced is no accident. People built every shade of that tone, scoring answers one at a time. What we call an AI's "personality" is really the sum of thousands of human decisions that gave it one.
What I looked for when auditing an output
Now let me move to the technical side of the work. Besides training models, one of my main jobs was auditing the outputs models produced. This audit isn't a casual "I like it, I don't like it." It's done against specific criteria, with each criterion scored separately.
We evaluated every output on several axes and marked each axis on a three-level error scale. No Issue, meaning nothing is wrong. Minor Issue, meaning there is a small flaw. Major Issue, meaning there is a serious error. When an output got a Major Issue, we stopped, dug into the error and reported point by point what broke and where.
These were the main axes we audited.
- Instruction Following. Did the model actually follow the instructions it was given, or did it do its own thing?
- Truthfulness / Trustfulness. Is the information it gave actually correct, or did it make something up in a confident tone?
- Groundedness. Can it base what it says on a real source, or is it talking out of thin air?
- Citation / Source Accuracy. Are its links real and from the right source, or is it inventing an address that doesn't exist?
- Harmlessness / Safety. Does it let itself be used for a harmful request, or does it hold the line?
- Coherence. Does the answer contradict itself, or does it hold together from start to finish?
- Relevance. Did it answer what was asked, or did it wander off topic?
- Verbosity. Did it drag on for no reason, or leave things out?
- Tone & Style. Did it strike the right tone, neither cold and robotic nor artificially warm?
- Language Quality. Are the sentences well formed, fluent, natural?
We wrote detailed prompts on different subjects, within different frames. Then we ran the model's output through every one of these axes. Did it stay faithful to the prompt? Did it make up information? Is the information it gave correct? Are the links it gave real? Is it pulling from the right source? For an output to count as reliable, it had to pass cleanly through every one of these gates.
Doing this work, here's what I understood. An AI's reliability doesn't start inside the model. It starts with the care of the person auditing it. However advanced a model is, it can't be fully trusted without an eye checking its output. And the sharper that eye, the sounder the model. So when I tell you to "check" what AI gives you, it isn't a slogan. It comes from work I did myself. I saw with my own eyes where models stumble.
AI that speaks Turkish carries my mark too
Let me tell you about the most personal part of this page. I didn't only train models in general. I also trained AI models for Turkish fluency, natural conversation and the correct use of Turkish idioms.
Let me explain what that means. When an AI speaks Turkish, its sentences often read as if they were translated from English. The idioms sound artificial, the word choice is off, and the tone isn't how a native Turkish speaker talks, it's how a machine translates. Closing that gap was also my job. I taught models proper Turkish, natural idioms, the expressions we Turkish speakers actually use.
The result is this. Some of the AI models people use today bear my mark. I played a part in the accuracy of their answers, in how well they use Turkish, and in how consistent their character is. So when you ask an AI something in Turkish and get a fluent, natural, local-sounding answer, that didn't happen by itself. Someone taught the model that. For some models, that someone was me.
I'm telling you this so you can see that AI didn't fall from the sky. The next time you talk to a model in Turkish, keep this in the back of your mind. Someone corrected this model's Turkish. Someone gave it this character. That someone was me. AI is a tool with human work behind it. And part of that work belongs to the person talking to you on this site.
What doing this work taught me
Now we come to the most important part. Once you've trained an AI yourself, you see using one from the outside in a completely different light.
You see the model not as an unquestionable mind whose every word is true, but as a trained tool. When it speaks with certainty, you're not impressed, because you know how that certainty was built. When it agrees with you, you don't feel flattered, because you've seen where the urge to agree comes from. When it makes something up, you're not surprised, because you know that filling a gap with something plausible is in the nature of the training.
This isn't fear. Quite the opposite, it's confidence. A craftsman who knows how a tool is made doesn't fear it, but uses it properly. That's how I look at AI, and it's also the reason I started this site. To pass on to you what I've seen. Because knowing this tool from the inside isn't a privilege. It's a literacy everyone has a right to.
Where the urge to agree comes from
I need to share something I saw from the inside, because it directly affects your daily use. As a model is tuned toward the answers people find good, it also drifts toward the answers people find pleasing. People like answers that agree with them, that are fluent, that sound sure of themselves. And over time, the model learns this.
The result is this. Sometimes a model says something not because it's true, but because it's what you'd like to hear. It agrees with you, because agreeing scores high. It speaks with certainty, because a decisive tone inspires trust. This isn't malicious design. It's a natural side effect of the training process. A system that learns human preferences also learns human weaknesses.
Knowing this makes you stronger. A model agreeing with you doesn't mean you're right. A model speaking with confidence doesn't mean it's correct. Someone who knows where this tendency comes from asks, while reading an output, "does this feel right to me because it's actually true, or because I like it?" That question is the heart of AI literacy.
What changes once you know this
You're not going to train a model, true. But knowing how a model is trained lets you see every tool you use differently.
When you get an answer, you now know it didn't fall from the sky. Someone showed that model data, taught it a tone, set a balance. That answer carries the mark of that training. Someone who knows this doesn't give an output blind trust. They give it calibrated trust. They don't mistake the model's certainty for accuracy. They don't mistake its agreement for confirmation. They sense where it falls short.
That is AI literacy. Being able to use the tool through the eyes of the person who built it. I lay the groundwork for this in a separate piece on understanding AI, the main page of my AI literacy work. There I explain from scratch what AI is, how it "thinks" and why it makes things up. This article fills in one part of that picture, through the eyes of someone who did the work.
Whoever knows how an AI is trained asks it the right question. Whoever asks the right question reaches the right answer. Know the tool, then use it.
Frequently Asked Questions
What does an AI model trainer do?
They shape how an AI model behaves using data and human feedback. They show the model sample answers, score and correct the answers it produces, and mark which answer is more accurate, clearer or safer. They are not the engineer who codes the model from scratch. They are the person who points it in the right direction and says "this is a good answer." This work largely decides an AI's tone and character.
How exactly is an AI trained?
In three layers. First the model reads an enormous amount of text and learns the patterns of language, which is called pretraining. Then it is steered with examples, so it moves from predicting words to answering questions. Finally, people read the model's answers, compare them and score them, and the model learns from these human preferences. The technical name for this last stage is RLHF, and it's where the model's character is shaped most.
So people are the ones training AI?
Largely, yes. The model picks up patterns from text on its own, but people show it what a good answer and a bad answer look like. People read answers, rank them, correct them, flag the harmful ones. I did this work myself. An AI that comes across as polite and balanced is no accident. That tone comes from the decisions of the people who scored it, one answer at a time.
Did Cem Ünsal really train AI models?
Yes. I trained models, audited outputs, and trained models for Turkish fluency. I ran outputs through axes like Instruction Following, Truthfulness, Groundedness and Safety, and scored each one as No Issue, Minor Issue or Major Issue. For confidentiality reasons I don't share client or project details, but I speak openly about the process itself and what this work taught me. What I write on this site rests on experience I lived firsthand, not something I read about from the outside.
What exactly do you look at when auditing a model?
The output is evaluated on several axes, each one separately. Instruction Following, Truthfulness, Groundedness and Citation Accuracy, Safety, Coherence, Relevance, Verbosity, Tone, and language quality. Each axis is marked No Issue, Minor Issue or Major Issue. For an output to count as reliable, it has to pass cleanly through every one of these gates.
Why does AI keep agreeing with me, and why does it always sound so sure?
Because during training, people like answers that agree with them and sound confident, and over time the model learns this. When it agrees with you, that doesn't mean you're right. When it sounds sure, that doesn't mean it's correct. Someone who knows where this tendency comes from asks, while reading an output, "is it like this because it's true, or because I like it?"
Could you really have shaped the Turkish of the AI I use?
For some models, yes. I also trained models for Turkish fluency, natural conversation and correct use of idioms. If an AI answers you in fluent, natural Turkish that doesn't read like a translation, someone taught it that. For some models, that person was me. I don't say this as a firm claim of ownership, but to show that there is human work behind AI.
What do I gain from knowing how a model is trained?
You see every tool you use differently. You know an answer didn't fall from the sky, that someone showed that model data and taught it a tone. Someone who knows this gives an output calibrated trust, not blind trust. They don't mistake certainty for accuracy or agreement for confirmation, and they sense where it falls short. That means using the tool through the eyes of the person who built it, which is the essence of AI literacy.