Transformers are neural network models which read through the whole sequence of words (or images or audio segments), find out the relation of each element with other elements, and create output based on that knowledge. It is the fundamental technology behind ChatGPT, Claude, Gemini, BERT, and most of the artificial intelligence models designed after 2018. The reason behind its name is the fact that transformers transform one sequence into another.
What Will I Learn?
Two Things Called “Transformer” — Which One Are You Looking For?
The word “transformer” means two completely different things depending on context.
AI/Machine Learning Transformer: A neural network architecture introduced in a 2017 research paper. Powers ChatGPT, Google Search, Claude, and most modern AI tools. This article focuses here.
Electrical Transformer: A physical device that steps AC voltage up or down using electromagnetic induction. Used in power grids and electronics. Jump to the electrical transformer section ↓
A transformer was definitely used by you today.
Every time you made a search on Google, a transformer would process your request. Every time ChatGPT answered you, a transformer was generating those words for you. Every time YouTube would recognize and write down speech from that video that you watched, a transformer was at work.
Transformer technology is ubiquitous, yet most people don’t even know about it. This guide will change it.
What Is a Transformer in AI?
A transformer is a neural network which analyzes sequences – whether texts, code, audio data or images – in a way which takes into account all aspects of the data at once and determines their relations to one another.
This is the crucial aspect of transformers. Unlike previous machine learning models, transformers did not analyze texts as a person who suffers from amnesia: only reading one word and then another while forgetting all the previous words. Transformers read the entire sentence at once, analyzing each word through its relation to all other words simultaneously.
As you can see, transformers had a deeper understanding of the context than any other previous models did.
The concept of transformer came from a paper by eight Google Brain researchers published in 2017 – Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Illia Polosukhin. The title of the paper was “Attention Is All You Need”. This bold, confident and somewhat provocative title proved itself right – it’s currently one of the most cited papers in the history of computer science, having citations in tens of thousands numbers.
One paper, eight coauthors – the beginning of the era of Artificial Intelligence triggered by it.
The Problem Transformers Solved
For understanding the importance of the transformers, first of all, it is essential to learn about how things were done before them.
The most popular model until the middle of the 2010s was called Recurrent Neural Network or RNN. In an RNN, text was read linearly: character by character and from left to right. RNN had something called “memory” that was continuously updated with each subsequent letter. This was a logical approach but it didn’t work well on long texts. Just think about it. If an artificial intelligence was reading the following sentence:
“The contract signed between the two parties in 2019, after three rounds of negotiation involving their respective legal teams, was eventually ruled invalid.”
Once the RNN has reached “invalid,” it has also partially forgotten what “contract” meant. The sentence starts with something that determines everything else in it. This phenomenon is known as vanishing gradients and was the reason why RNNs performed poorly in cases where a long-term memory was required.
Engineers introduced LSTMs (Long Short-Term Memory networks) with additional memory gates that improved the situation. However, LSTMs were still based on sequential processing, which made training slow and prevented parallel computing. Adding more GPUs did not solve the issue.
The information bottleneck problem existed as well. The earlier encoder-decoder architecture compressed an entire input sentence into one fixed-length vector prior to the creation of an output sequence. It was okay for simple sentences, but for everything else, it was like cramming a book through a keyhole. Transformers got rid of sequential processing completely.
The Breakthrough: “Attention Is All You Need” (2017)
The key idea put forward by Vaswani et al. in their paper is this: No recurrence is required. No need to read words sequentially. Let each word in the sentence look at every other word — and learn which ones are really important for understanding the meaning of the current word.
This is what they referred to as the attention mechanism.
It may surprise you but the attention mechanism existed before Vaswani et al.’s work — it was used as an auxiliary mechanism in previous models. What the 2017 paper demonstrated correctly was that this mechanism is enough by itself, without any additional mechanisms.
The practical benefit was huge. Processing sentences in parallel means no sequential dependencies. Thus, models that would have taken weeks to train were trained within days. What used to be unfeasible in terms of computation became possible.
The paper was published in December 2017 during NeurIPS. In 2018, Google had already released BERT. OpenAI released GPT-3 in 2020. In late 2022, ChatGPT got 100 million users in just two months. All this was made possible because of this single 2017 paper.
How Does a Transformer Work?
There’s one problem common to all technical descriptions of AI: they tend to begin with mathematics. Vector queries, vector keys, softmax functions. It’s helpful for those trying to implement a transformer, but useless for anyone who is not.
So let us first introduce the idea of a transformer. The math will be explained later.
The processing of input in a transformer consists of approximately four phases:
- Tokenizing: splitting the input into parts (tokens), usually into words and sub-words.
- Embedding: mapping each token onto a sequence of numbers describing its meaning.
- Attention: letting each token examine all other tokens and determine their significance.
- Prediction: using this knowledge to generate output (a word, a translation, a classification).
Phase three, attention, is what makes transformers unique.
Self-Attention — How the Model “Reads”
Imagine a word trying to make sense of itself through its surroundings.
Consider the word “bank” used in two different sentences:
- “She walked along the river bank.”
- “She deposited money at the bank.”
The word “bank” is the same in both sentences, but its meaning is not. To a human reader, context solves the ambiguity right away. The sequential approach will have trouble because it relies only on the previous context. In contrast, the transformer network makes short work of this ambiguity; it uses attention and gives the word “bank” a chance to look at all the other words in the sentence at once and see the “river” or “deposited” as the clue.
Mathematically, this is achieved by creating three derived representations from each token: Query (information that this token is interested in), Key (information that this token offers to other tokens), and Value (actual information the token contributes). Then, similarities between queries and keys are calculated and used as weights for the values.
The formula looks like this: Attention(Q, K, V) = softmax(QK^T / √d_k) · V
Don’t worry too much about the formula. The intuition is: every word votes on how much it cares about every other word, and the model learns those voting weights during training.
Multi-Head Attention — Reading in Parallel
A single attention mechanism suffices, but natural language has several levels of relation processing occurring at once.
For example, in the sentence “The chef who trained under Bocuse opened her restaurant in Lyon,” it is important to keep track of who the subject of “opened” is (the chef and not Bocuse), who “her” is referring to (the chef, who is female), and where the restaurant is located (in Lyon). These are three different kinds of relations – subject-verb, pronoun-antecedent, and location respectively.
With multi-head attention, several attention mechanisms process different kinds of relations, and their outputs are aggregated together. The analogy here would be multiple editors reviewing the same document at once and aggregating the notes they took while doing so.
The number of heads differs from model to model – BERT-base uses 12, larger models use 64 or even more.
Positional Encoding — Knowing Word Order
And here is the uncomfortable truth about transformers: they operate on all tokens simultaneously. In other words, by definition, they do not know the sequence in which the words appeared.
“Dog bites man” and “Man bites dog” consist of the exact same tokens. Only the sequence makes them distinct. A transformer that does not distinguish between sequences will interpret them as one and the same thing.
The answer is positional encoding: adding a position marker to the vector of each token before the processing starts. The 2017 paper created the positions using sine and cosine with varying frequencies for each.
In summary, the transformer now knows the position of the tokens without having to process them sequentially. Parallelism is preserved.
Encoder and Decoder — The Input/Output Pipeline
The earliest transformer models — including the 2017 version — had an encoder-decoder architecture. The encoder processes the input data and gains its understanding. The decoder outputs the response, paying attention to the encoder’s output and its own partial output.
Translation is the classic case. Input English sentences get fed to the encoder. The decoder produces French translations of them, paying attention to the encoder’s contextually-aware understanding of the English input.
Not all transformer models have both, though:
- Encoder-only models like BERT are built for understanding text, not generating it. They work well for classification tasks, searching, and Q&A.
- Decoder-only models like GPT and Claude are built for generating text based on preceding tokens. These are what people typically interact with nowadays.
- Encoder-decoder models like T5 and BART keep both components alive. They’re still widely used in translation and summarization pipelines.
Transformer vs. RNN vs. CNN — What’s Actually Different?
| Transformer | RNN / LSTM | CNN | |
| Processing style | All tokens at once (parallel) | One token at a time (sequential) | Local windows / patches |
| Context range | Full sequence (any distance) | Degrades over long sequences | Local only (limited range) |
| Training speed | Fast (parallelizable) | Slow (sequential dependency) | Fast |
| Best at | Language, vision, multimodal | Short sequences, time series | Images, local pattern detection |
| Main weakness | High memory usage; quadratic scaling with sequence length | Vanishing gradients; slow over long sequences | Doesn’t capture global context naturally |
| Still widely used? | Yes — dominant in NLP and increasingly vision | Yes — for time series, some edge deployments | Yes — for image processing, often combined with transformers |
One important clarification to be made here: CNNs were never language models from the beginning, so it does not make much sense to compare them to transformers in terms of their performance on NLP tasks. But in the case of computer vision, the battle is on; ViTs have been competing with CNNs successfully since 2021 on many benchmarks.
The Transformer Models Powering AI in 2026
This is what most explainer articles skip — and it’s the most useful thing to know.
All of these are transformers. If you’ve used any of them, you’ve already used a transformer architecture.
Encoder-Only: BERT and Its Family
BERT It was released by Google at the end of 2018. Unlike other transformer-based models, BERT scans text in both directions at once – from left to right and vice versa. That is why it is great at contextual understanding.
BERT does not generate text. It comprehends it. Google applies BERT (or its descendants based on the same algorithm) to comprehend the meaning of the search query, rather than its keywords. “Python not working” is understood as the programming language, not the reptile, thanks to models like BERT.
RoBERTa, ALBERT, and DeBERTa are the most prominent descendants of the initial model.
Decoder-Only: GPT, Claude, Gemini, Llama
Here are the main examples of models people refer to as “AI” in 2026.
GPT (Generative Pretrained Transformer), created by OpenAI, processes information from left to right and predicts the next token. The GPT-3 version of the model had 175 billion parameters. GPT-4 and its newer versions are even bigger and better, although the number of parameters is not public knowledge.
Claude, an AI created by Anthropic (the company behind this guide), is also a decoder-only transformer architecture. Similar architecture, but different training strategy, alignment with values, and design approach.
Google DeepMind’s Gemini family of models is based on the transformer architecture and built natively for multimodal tasks such as working with text, images, audio, and code in one model.
Meta’s Llama family of models is an open-source language model architecture that gives powerful transformers to developers who want to work with them locally. This is more important than many people think because it opened up access to frontier-scale transformer architectures to everyone.
In practice, though: when you type a prompt into any of these tools and watch words appear one by one, that’s not a recording being played back. The model is computing the most likely next token, then the next, then the next — in real time — using the transformer’s attention mechanism to keep the whole conversation in context. Every word you see is a prediction. A very good prediction, but a prediction.
Encoder-Decoder: T5, BART, and Translation Models
T5 (Text-To-Text Transfer Transformer) developed by Google approaches each NLP problem in terms of text-to-text problems. Whether it is summarization, translation, question-answering, or even classification, everything is formulated as “given this text, generate that text.” Simple and incredibly effective.
BART by Meta incorporates a bidirectional encoder architecture (similar to BERT) along with an autoregressive decoder architecture (similar to GPT). In particular, it excels in situations when understanding the complete context as well as generating output texts are needed – summarization of documents being one example.
Most commercial translation APIs – Google Translate, DeepL – are based on the encoder-decoder transformer architectures.
What Are Transformers Used For?
The quick answer: most of what you would consider “AI” nowadays.
Text generation and conversation — ChatGPT, Claude, Gemini and their peers are all based on decoder-only transformers, that produce answers token by token using attention to take into account the entire context of the conversation.
Search — Since the integration of BERT in 2019, Google’s search ranking uses transformer-based language understanding. Both your search query and the websites considered to be candidates for appearing on the results are evaluated with the help of transformer models, not just keywords, but understanding of semantics.
Code generation — GitHub Copilot, Cursor and others generate the code based on transformer models fine-tuned on the codebase. They can complete functions, fix bugs and generate tests, because the code, like text, is a structured sequence.
Machine translation — DeepL and Google Translate use transformer-based models to translate between languages. The leap in quality from the pre-transformer era to the modern day is really impressive if you have some experience of using machine translation.
Image synthesis and interpretation — ViT entered the competitive arena against CNNs by viewing an image as a set of patches and conducting self-attention among them. DALL-E and Stable Diffusion rely heavily on transformers.
Speech recognition — The Whisper model by OpenAI is a transformer model that learns from audio spectrograms. When YouTube automatically generates captions for a video, it employs a transformer.
Yes. It’s six key application areas — and the list never ends.
Limitations of Transformer Models
I think the limitations section is more useful than the capabilities section. Knowing where a tool breaks down tells you more about how to use it than knowing what it can do.
1. Compute cost — and the quadratic problem
Attention is costly. Every single token pays attention to every other token. Hence, the cost of computation is squared with respect to the sequence length. Double the size of the context window; quadruple the computation power required. That is why the early models were limited in terms of the context window size, and overcoming such a limit was difficult.
By 2026, the best performing models will be equipped with a context window spanning hundreds of thousands of tokens. However, reaching that point was no easy feat and required novel architectural designs such as sparse attention, sliding window attention, and alternatives to self-attention mechanisms.
The academic community has done extensive research on alternative architectures. SSMs, including Mamba, process sequences in linear fashion compared to quadratic for traditional transformers. It is too early to say whether this architecture will replace transformers or not.
2. Hallucination
Transformers predict the next token that is the most probable from a statistical perspective. They do not “know” anything in the same way as humans do. Instead, they match patterns in an extremely massive scale. However, when the pattern doesn’t work – if they are asked about something rare, new, or just outside their training – they come up with absolute nonsense confidently. This phenomenon is known as hallucination, and as of 2026, there is no solution for it.
Retrieval-Augmented Generation (RAG) is currently the best way to address the problem: make sure that the model can retrieve additional documents during the inference process.
3. Context window limits (and what happens at the edges)
Despite the use of extensive window sizes for transformers, their performance is affected when dealing with extremely long texts. It has been observed that transformers are less likely to attend to the information located at the center of an extensive text than the start or end of it.
4. Data hunger
Transformers require large amounts of training data. GPT-class models require hundreds of billions to trillions of tokens of text. This is costly and increasingly calls into question the sources of the training data. While smaller models using carefully curated data have narrowed the gap somewhat, the basic desire for data still exists.
5. Mixture of Experts as one response
A solution to the efficiency issue within architecture is Mixtures of Experts (MoE). Instead of having all model parameters activated at once for every input, MoE models allow for each token to be processed by only a small fraction of experts. It is widely speculated that GPT-4 employs MoE; however, there has been no official confirmation from OpenAI.
Electrical Transformer
An electrical transformer is an apparatus which converts the voltage of an alternating current without varying the frequency of the AC.
It finds applications in power grids, electronics adaptors, and industrial machinery.
Principle of operation: The principle employed in the construction of an electric transformer is Electromagnetic Induction discovered by Michael Faraday in 1831. On flowing of AC in the primary coil, a variable magnetic flux is generated. This variable magnetic flux induces a voltage in the secondary coil.
The two main types:
- Step-up transformer — more turns in the secondary coil than the primary; output voltage is higher than input. Used at power generation stations to boost voltage for long-distance transmission (high voltage = lower current = less energy lost as heat).
- Step-down transformer — fewer turns in the secondary coil; output voltage is lower. Used at substations and in the adapter bricks that charge your phone.
The transformer found in your phone charger is a step-down transformer. The huge transformers that can be seen along power lines outside your community are two types of transformers: the step-up transformer increases voltage from the power plant to very high voltages; then, substations gradually decrease it until 120V or 240V reaches your plug.
The core of a transformer is normally made up of laminated iron since it gives the flux an easy route. There are also air-core transformers used in high frequency operations.
Frequently Asked Questions
Q1. Is ChatGPT a transformer model?
Ans. Yes. ChatGPT uses the GPT-4 or future versions of GPT-4 language models that consist of a decoder only transformer. As you enter your message, the entire conversation context is processed with the help of self-attention, and then the reply is generated from it one token at a time.
Q2. Who invented the transformer in AI?
Ans. The transformer model was created by eight authors including Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Illia Polosukhin in an article called “Attention Is All You Need,” which was written in 2017 when the authors were working for Google Brain and Google Research.
Q3. What is the difference between BERT and GPT?
Ans. They are both transformer-based models, but have distinct uses. BERT is an encoder only model that is bidirectional, reading its inputs from both sides in order to comprehend them; therefore, it is best for tasks of classification, searching, and comprehension. GPT is a decoder only model that reads left-to-right and predicts the next token; thus, it is best for generating language.
Q4. What is self-attention in a transformer?
Ans. The ability of self-attention is the one that enables all tokens within a sequence to attend to each other in order to find out which tokens are important in regard to the current one. Each token creates three types of vectors: Query vector (what is being sought), Key vector (what is offered), and Value vector (token content).
Q5. Can transformers process images?
Ans. Yes. Vision Transformers (ViT) divide images into patches of a fixed size, treat these patches as tokens, and perform self-attention. In this way, it allows the model to learn spatial dependencies from images in the absence of the local filters used in CNN. Most image generation and recognition models currently incorporate transformers, either alone or along with convolutional layers.
Q6. What is a step-up transformer? (Electrical)
Ans. The step-up transformer boosts the input AC voltage to output AC voltage. It has more winding in the secondary than in the primary. Since the product of voltage and current is constant in an ideal transformer, an increase in voltage is accompanied by an equal decrease in current. Power lines employ the step-up transformer to reduce the current hence heat dissipation due to resistance.
Q7. How is an AI transformer different from an electrical transformer?
Ans. It’s a coincidence of nomenclature. AI transformers are neural network architectures that use attention to process data sequences. Electrical transformers, on the other hand, are physical devices consisting of copper windings and an iron core that transform AC voltages using electromagnetic induction. The commonality between them lies in their names; they both “transform,” although in different ways.
Q8. What are the main limitations of transformer models?Q1. Is ChatGPT a transformer model?
Ans. The main challenges for deployment include: (1) quadratic complexity with respect to sequence length, which means that very long contexts become costly; (2) hallucination, which is when wrong but confidently generated information appears; (3) extensive training data needs; and (4) higher inference costs compared to smaller models. There is an ongoing effort in addressing all of these challenges, with such techniques as Mixture of Experts, State Space Models, and Retrieval-Augmented Generation gaining some ground.
Where This Goes Next
However, the transformers’ evolution is far from complete. In 2026, the research front will not be occupied by studies on attention’s effectiveness. The key research questions are efficiency issues: how can one achieve performance comparable to that of transformers but using orders of magnitude less computation? Mamba and SSMs provide an answer. MoE provides another answer. Hybrid models combining attention with something else is another.
And the truth is that nobody really knows yet what will come out of those lines of inquiry. That’s the cool thing about them.
However, there is a more pressing question: as transformers become an invisible foundation of our AI-powered world – embedded into search and tools and software we don’t even notice – what does it mean to understand them?
Not necessarily in a mathematical sense. Just understanding what it is, what it does wrong, and when it should be trusted.
You already have the answer to this one.
