Transformers in artificial intelligence: how they work and why they changed the world
When you first heard about artificial intelligence, you probably thought of robots, self-driving cars, or voice assistants. But the real revolution did not begin with the metal arms of machines, but with models capable of understanding language.
Today, AI can read, write, translate, and even converse. And it is precisely transformers that made this possible.
Before transformers emerged, however, the road was long and winding. For years, scientists tried to teach computers to understand text, but every approach had its limitations. Recurrent neural networks (RNNs) and their more advanced variants (LSTMs and GRUs) could process sequences of words, but struggled with long sentences. The model lost the meaning when the context extended too far.
And then, in 2017, a research paper was created in Google’s laboratories, ushering in a new era. It bore a simple yet meaningful title: “Attention Is All You Need”.
It was there that the transformer was born—the model that redefined language processing and became the foundation of everything we now call generative artificial intelligence.
Table of contents
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The attention mechanism, or how the model “focuses” on meaning
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Tokens, embeddings, and positions – how the model “sees” text
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Summary: why the transformer is the most important invention in the history of AI
What exactly is a transformer?
In short: a transformer is a neural network architecture designed to learn relationships between elements in a sequence, such as words in a sentence. Its primary goal is to understand context.
It may not sound particularly groundbreaking, but the way the transformer does it is ingenious. Unlike earlier models, which analyzed text word by word, the transformer looks at the entire sentence simultaneously.
This allows it to understand that in the sentence “The dog that chased the cat was black,” the word “that” refers to the dog, not the cat—even though several other words appear between them.
The key to this is something we call the attention mechanism (English: attention).
https://en.wikipedia.org/wiki/Attention_Is_All_You_Need
The Attention Mechanism, or How a Model “Focuses” on Meaning
Imagine that you are reading a sentence. Your brain does not process every word in isolation. Instead, it constantly refers to the context. When you see the word “which,” you automatically recall what was discussed earlier.
Transformers do something very similar. The attention mechanism allows them to “look” at all the words in a sentence and decide which ones are relevant to one another.
Mathematically speaking, each token (that is, a fragment of text, such as a word or part of a word) is assigned a numerical vector describing its meaning. The model then calculates the so-called attention weights—that is, values determining how much a given word influences the interpretation of another.
It is precisely these weights that enable the model to capture the relationships, meanings, and emotions embedded in language.
Thanks to this, the transformer can understand the difference between sentences:
- “Jan loves Maria”
- “Maria is loved by Jan”
Although both sentences contain the same words, their meaning is different—and the model can distinguish between them.
Transformer Architecture Step by Step
Every transformer consists of two main parts: the encoder and the decoder. Put simply, this can be compared to two stages of thinking:
- Encoder understands what was said.
- Decoder responds with what should be said in response.
Encoder – understanding the context
The encoder takes input data (e.g., a sentence in English) and transforms it into so-called internal representations—a set of numbers that describe the meaning of words and their relationships to one another.
Each encoder layer consists of:
- Multi-Head Attention mechanism – multiple independent “attention” mechanisms that learn different aspects of a sentence (e.g., syntax, meaning, and emotional relationships).
- Feed-Forward Network – a simple neural network that processes the data after attention.
- Normalization and residual connections – thanks to them, the model learns stably and does not “forget” information from previous layers.
Decoder – generating responses
The decoder works similarly, but it has an additional role: based on the encoder’s data, it generates an output, such as a translation or an answer. When generating each word, the decoder uses what it has already written and the information provided by the encoder.
In practice, this means that the model not only understands what the sentence is about, but also knows how to continue the thought logically and linguistically.
Tokens, Embeddings, and Positions – How a Model “Sees” Text
Words have meaning for humans. For a computer, everything is numbers. Therefore, before a transformer can begin working, the text must be converted into a numerical form.
Tokenization
The first step is tokenization, that is, dividing text into smaller units (tokens). A token can be an entire word, a syllable, or even a single letter. Thanks to tokenization, the model can understand both common words (“house”) and rare forms (“little house,” “homestead”).
Embeddings – significance in numbers
Each token is then transformed into a numerical vector called an embedding. It is somewhat like coordinates in a space of meanings. In this space, the word king is close to queen and far from dog. This allows the model to understand that the relationship between king and queen is one of gender, not, for example, species.
Positional encoding
Because the transformer does not process text sequentially (like humans or RNNs), it needs information about the position of each word in a sentence. This is where positional encoding comes in: adding information about its position in the sequence to each vector. Thanks to this, the model knows that in the sentence “The cat ate the fish,” the cat was the agent, not the victim.
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Why Are Transformers So Effective
A transformer's success depends not only on its architecture, but also on how well it scales with data.
Parallel Computing
Transformers can process all words simultaneously. This makes training much faster than in older models, which had to analyze text sequentially.
Long dependencies
The model can “remember” context over long stretches. It can therefore understand the meaning of an entire paragraph, rather than just a single sentence.
Flexibility
The same architecture works across different domains: language, images, sound, code, and biology. This has made transformers a universal blueprint for modern AI.
How Transformers Learn
Training a transformer involves prediction—but not of the future, only of missing words.
BERT-type Models
BERT learns through masking. The model is given a sentence with a hidden word: “Jan went to the [MASK] for bread.” Its task is to guess what should go there. This allows it to learn the meanings and relationships between words.
GPT-type models
GPT, on the other hand, learns by predicting the next word. It is given the beginning of a sentence: “When the sun sets…” and must predict what comes next. In this way, it learns the structure of language and a logical train of thought.
Training on Billions of Words
Modern models are trained on gigantic datasets: books, articles, and websites. The more data, the better the model can capture the nuances of language.
From BERT to ChatGPT: The Evolution of Transformers
After Google published the article, the AI world exploded.
- 2018 – BERT (Google): a revolution in language understanding. Models began to understand the meaning of sentences, rather than merely counting words.
- 2019 – GPT-2 (OpenAI): the first large models generating text with surprisingly coherent meaning.
- 2020 – GPT-3: 175 billion parameters and a breakthrough in writing, coding, and translation.
- 2022 – ChatGPT: the use of Reinforcement Learning from Human Feedback (RLHF), which enabled the model to learn from human evaluations and hold conversations.
- 2023–2025 – The multimodal era: Gemini, Claude, GPT-4o, and other models began to understand not only text, but also images, audio, and even video.
Today, transformers are not just a tool—they are the foundation of the entire artificial intelligence ecosystem.
Transformers Beyond Language
Although they were created for language, they quickly found applications in other fields.
Vision Transformer (ViT)
In 2020, Google introduced the Vision Transformer—a model that, instead of processing pixels like a conventional convolutional network, divides an image into “tokens” (small fragments) and analyzes their relationships in the same way as words in a sentence. It turned out that the transformer could recognize objects, colors, and patterns exceptionally well—often better than earlier algorithms.
Audio and music
Transformers also analyze sound. They can recognize speech, generate music, and even synthesize voice in real time.
Biology and Science
DeepMind’s AlphaFold model used similar principles to predict protein structures. This discovery was called one of the greatest breakthroughs in 21st-century biology.
Transformers in Practice: From Google to Your Laptop
If you use a search engine, translator, voice assistant, or ChatGPT, you are using a transformer.
Search Engines
Google has been using BERT-based models for years to better understand queries. As a result, when you type “how to cook rice so it doesn’t stick,” the system understands the intent, not just the words.
Translations
Translation systems (Google Translate, DeepL) use transformer decoders to translate sentences while preserving meaning and grammar.
Chatbots and Assistants
ChatGPT, Gemini, Claude – all these systems are powerful transformer networks capable not only of answering, but also of analyzing, summarizing, and translating.
The Future of Transformers
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The development of transformers is not slowing down. On the contrary—it is only just beginning.
Multimodal models
New models can understand text, images, audio, and even video simultaneously. This means that in the future, AI will be able to analyze reality in its entirety, rather than just a fragment of it.
Specialized models
Instead of one gigantic model for everything, a trend known as the mixture of experts is emerging—a set of specialized models that work together.
Local Transformers
Thanks to advances in compression and optimization, smaller versions of transformers (e.g., LLaMA 3, Mistral, Phi 3) are beginning to run directly on laptops and smartphones.
Do Transformers Really “Understand” Language?
This is a question that keeps coming back like a boomerang. From a technical point of view—no, transformers are not conscious. But in practical terms, they can operate language so well that the distinction begins to blur.
The model does not know what a “dog” is, but it understands how people use the word. That is enough to write texts, translate contexts, and solve problems.
In a sense, transformers do not imitate humans—they create their own way of understanding language.
Summary: Why the Transformer Is the Most Important Invention in the History of AI
Transformers are not just another step in the development of artificial intelligence. They are a leap forward by an entire generation. They have changed the way we teach machines, think about language, and create technology.
Thanks to them:
- AI can understand context and meaning.
- Models can be trained in parallel on vast datasets.
- The technology has become more accessible—from education to business.
It was transformers that made it possible to create large language models such as ChatGPT and Gemini, ushering in a new era—one in which communication with machines becomes natural.
And although much still lies ahead of us, one thing is certain: without the Transformer, modern artificial intelligence would not exist.