How Does Artificial Intelligence Actually Learn? Training Artificial Intelligence for Non-Technical People
We constantly hear that artificial intelligence is trained, that models are taught using data, and that this consumes a great deal of energy and computing power.
The thing is, to people outside the world of machine learning, it sounds a bit like black magic. What does training an AI model really mean, and what does it involve in the case of language?
I will try to explain this as simply as possible, while not shying away from technical details where they are truly important. I will show why word vectors have become the foundation of modern language models, what the training process looks like, and what follows from it—including for understanding the limitations of artificial intelligence.
Table of Contents
We Convert Words into Numbers, and Then into Vectors in Multidimensional Space.
Let’s start with the basics. A computer cannot operate on words as such. To it, “cat” or “dog” is nothing more than a string of characters. For a language model to do anything with them, these words need to be converted into numbers.
The simplest method would be to assign a single number to each word, but this provides no insight into the relationships between them. Therefore, in practice, vectors are used—sets of numbers that describe a word in a multidimensional space.
You can think of it as coordinates on a map. Except that instead of two or three dimensions (such as length, width, and height), we have hundreds, and sometimes even thousands, of dimensions. Each word is a point in this vast space. As a result, words with similar meanings are located close to one another, while different ones are far apart.
Why Is Visualization So Difficult?
In mathematics, adding dimensions is nothing complicated. You can always add another axis and another coordinate. The problem begins when you want to see it. Our brain is excellent at handling three dimensions, but beyond that, it is helpless.
That is why scientists and engineers often use dimensionality reduction methods that reduce a space from, for example, a thousand dimensions to two or three. This makes it possible to draw a plot, although it is important to remember that this is only an approximation.
An excellent tool that demonstrates this is Google’s embedding visualizer. You can enter a word there, e.g. football, and the system will highlight the 100 closest words in vector space. Suddenly, you see not only obvious associations, such as playoff, ncaa, or olympic, but also less predictable ones, such as comedian or guitarist.
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Where Do Surprising Adjacencies Come From?
At first glance, it is difficult to understand what a comedian has to do with football. However, it is enough to remember how vast and diverse the training data is. Models learn from texts on the internet, books, press articles, forums, and social media.
If, in such sources, someone created lists of occupations, interests, or descriptions of people’s profiles, then footballer, guitarist, and comedian could appear alongside one another. The model “remembered” that these words often co-occur and consequently considered them closer than intuition would suggest.
Such phenomena are important because they help explain, among other things, AI hallucinations—situations in which a model connects things that, from a human perspective, do not fit together at all.
What does the training process itself look like?
So far, we have talked about what vectors are and how they can be visualized. But how does AI arrive at “knowing” where to position a given word?
At first, all parameters are random. The words are then scattered chaotically in space. The model is given a task—for example, to predict the missing word in the sentence: “I bought a ball and went to play ___.”
If they guess that it should be “soccer” or “football,” they get a point. If they make a mistake and give, for example, “chess,” the difference between their answer and the correct result is used to adjust the parameters.
This mechanism, known as backpropagation, is repeated billions of times. Step by step, the model adjusts its parameters (weights and biases) until it finally begins to accurately capture the patterns present in the data.
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Weights and Biases – Two Pillars of Learning
Model parameters are divided into two main categories:
- weights – determine the “strength” of connections between neurons in the network and the position of vectors in space;
- biases – act as offsets that allow the model to capture context and nuances.
Thanks to weights, the model “knows” that the word dog is closer to the word cat than to the word car. Thanks to biases, it can detect that in the sentence “I put on a jacket,” the word “put on” refers to clothing, not, for example, a chair.
Attention Mechanism – How the Model “Looks” at Text
Modern models, such as GPT, use another key element: the attention mechanism. It allows them to decide which parts of the text are most important for a given word.
If a model analyzes the sentence “The car that I bought yesterday turned out to be red,” when processing the word “red,” it should look at “car,” not “yesterday.” This is precisely what attention controls: which fragments are connected to one another.
It can be compared to a conversation—you sometimes focus on one thread while ignoring others. AI does something similar, but mathematically, using vectors and matrices.
Why does training require so much computing power?
When you hear that training a large model takes weeks and requires hundreds of graphics processors, it is precisely this process of repeatedly adjusting the parameters that is being referred to.
Each iteration involves computations on enormous matrices. And there are billions of such iterations. That is why, without modern graphics cards capable of performing thousands of operations in parallel, practical AI training at this scale would be impossible.
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Why is this important to us, the users?
You may ask: “Why do I need this knowledge if I’m not planning to train models myself?” The answer is simple: to better understand the limitations and capabilities of AI.
When a model provides an inaccurate answer or starts hallucinating, it is not due to laziness or ill will. It is a result of how its vectors and parameters are structured. It is worth bearing this in mind, especially when we use AI at work or in education.
Experiments with embedding visualizations also provide an intuitive understanding that the model does not think like a human—it operates on statistical patterns. It is a powerful tool, but it functions within its mathematical limitations.
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Fine-tuning, or tailoring to specific needs
This is precisely where the concept of fine-tuning comes in. Not every user has the time or resources to train a model from scratch. That would be like building an entire library just to learn a few specialized books. Fine-tuning works differently—we start with an already trained, general-purpose model (which knows the language and has a vast database of words and contexts), and then adapt it to a specific task.
For example, a law firm can take an existing language model and “train” it on a small set of its own documents to understand legal jargon, typical client questions, or the structure of legal opinions. The result? A model that still understands general language but performs better in a narrow field.
From the user's perspective, fine-tuning has several advantages:
- better accuracy – the model hallucinates less in your field because it has additional context;
- greater efficiency – there is no need to train everything from scratch, but merely to “fine-tune” the existing machine;
- personalization – the model can learn terminology specific to the company, industry, or communication style.
Why does it work?
Mechanically speaking, fine-tuning involves slightly adjusting the weights and biases in an already trained model. These enormous matrices of numbers are like a landscape with mountains and valleys already in place. Fine-tuning does not create a new world from scratch—it simply sculpts a few areas so that the landscape better matches our map.
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Fine-tuning in practice
Imagine that a general model knows that “football” is close to “playoff” and “NCAA,” but also—due to the data—close to “comedian.” If you want to use the model in an educational application for athletes, you can use fine-tuning to shift the vectors so that they emphasize sports contexts more strongly and ignore those that are less relevant.
Why is it worth knowing about?
Because fine-tuning explains why industry-specific and specialized versions of AI models exist. If you use a tool for legal, medical, or technical analysis, it is often precisely the result of fine-tuning a general-purpose model. Understanding this helps you choose tools more consciously—you know that a model “from the general internet” will have a different level of accuracy than one that has undergone additional training on data from your industry.
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RAG and knowledge bases – even simpler than fine-tuning
Although fine-tuning allows a model to be adapted to specialized applications, there is an even simpler method—RAG (Retrieval-Augmented Generation). In this approach, the model is neither further trained nor modified internally. Instead, it is connected to an external knowledge base from which it retrieves information in real time.
Imagine that the model is supposed to answer questions about the products in your company. Instead of teaching it all the descriptions and documentation from scratch, it is enough to connect it to a database where this information is stored. When a user asks a question, the model first “searches” the database and only then builds an answer based on it.
From the user’s point of view, this is a huge advantage: there is no need to wait weeks or invest significant resources in fine-tuning. RAG makes it quick and easy to ensure that AI is more up to date, reliable, and tailored to the context in which you work.
The simplest form of RAG is a custom model/assistant in ChatGPT. You can also simply add an organized PDF containing knowledge to a thread or project, e.g. in the form of a nested numbered list indicating which points the given text fragments refer to.
Where Is It All Heading?
AI model training is evolving in several directions simultaneously:
- larger models – an increasing number of parameters, allowing them to capture ever more subtle relationships;
- better data – cleaner, more diverse, and better annotated;
- more efficient algorithms – e.g. those that learn faster or consume less energy;
- specialization – models trained in narrower fields (medicine, law, education).
Each of these directions matters. But the foundation remains the same: adjusting vector parameters to best reproduce the patterns present in the data.
Summary
Training artificial intelligence is the process of adjusting numbers—weights and biases—that determine how words and contexts are arranged in a multidimensional space. Language models do not understand language the way humans do, but through billions of iterations, they learn to reproduce statistical patterns present in the data.
Tools such as interactive embedding visualizations offer a glimpse into what happens inside the model. They help us better understand both its strengths and its limitations.
And that is precisely why it is sometimes worth looking deeper. Because artificial intelligence, although based on complex mathematics, is essentially founded on a simple idea: learning through repetition and correcting mistakes until the numbers begin to form a meaningful picture of language.