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How Transformers Work: The Foundation of Modern AI (And Why You Should Care)

Transformers are everywhere. You see the results of their capabilities each time you ask ChatGPT a question, search for something on Google, translate a phrase into another language or get a code suggestion from GitHub Copilot. These models have become so embedded in our workflows that many people now rely on them daily, even professionally, without understanding how they actually function. This gap in comprehension is understandable, the systems are complex, but I believe it's a gap worth closing. If you're using AI tools regularly, whether as a developer, writer, marketer or educator, you should have at least a conceptual grasp of how their core mechanisms work.

That’s why I’ve written this blog. I want to offer a technically accurate but accessible look into how transformers function, from input to output and why their design marks such a radical leap from earlier AI systems. While the architecture can seem dense, I believe that with the right framing, and a few real-life examples, we can make sense of it together. Because once you understand transformers, you begin to see not just how these tools work, but why they behave the way they do. And that understanding matters, especially as these models become more capable and more influential in shaping the content, decisions and outcomes we interact with every day.

What is a Transformer?

A transformer is a type of deep learning model architecture introduced in the 2017 paper "Attention Is All You Need" by Vaswani et al. Before transformers, sequence models like RNNs and LSTMs were dominant in natural language processing. These models processed data step-by-step, which limited their ability to handle long-range dependencies and made them difficult to parallelise. Transformers, in contrast, process entire sequences at once using a mechanism called attention.

This shift away from sequential processing allowed transformers to scale efficiently. For example, while older models struggled to remember the beginning of a long paragraph when generating text, transformers can access the full context at every step. This ability is what allows GPT-4 to write multi-paragraph essays, translate documents or summarise entire web pages with remarkable fluency.

From Words to Numbers

Transformers do not read text as we do. Instead, every word or token is first converted into a numerical vector through a process known as embedding. These vectors capture semantic information. For instance, the words "king" and "queen" might be close in vector space, because they share similar meanings.

Let’s take a real-world example. When Netflix recommends a movie based on a description you typed, it converts your text into embeddings and compares it to embeddings of thousands of titles. The similarity between these numerical representations helps the system recommend something relevant.

Adding Structure: Positional Encoding

Because transformers process words simultaneously rather than in order, they require a way to retain information about word position. This is where positional encoding comes in. It adds signals to the embeddings to indicate where each word sits in the sequence.

Consider the sentence "The cat chased the mouse." Without positional encoding, the model might not know whether "chased" comes before or after "mouse." With it, the order is retained, allowing the transformer to understand the structure of the sentence and generate grammatically coherent responses.

Attention: The Core Innovation

The real magic of transformers lies in their use of self-attention, a mechanism that enables the model to evaluate the importance of every other word when interpreting a specific word. This ability to determine relevance within a broader context is a significant departure from earlier models, which typically processed information sequentially and could struggle with long-distance dependencies. In contrast, self-attention allows each word to attend to every other word in the sentence simultaneously, constructing a deeply contextualised representation.

To explain how this works technically, transformers create three vectors from each word: queries, keys, and values. These vectors interact through a mathematical process that calculates how much attention each word should pay to the others, assigning attention scores that act as weighted links between words. These scores are then used to produce a contextually aware output. Imagine reading a recipe: "Chop the onions. Then sauté them in butter." A transformer learns that "them" refers to "onions" by calculating attention scores between the pronoun and its likely antecedents. This is a relatively simple example, but the principle applies in more complex situations, such as legal reasoning, narrative structure, or conversational memory. The self-attention mechanism provides the backbone of this reasoning ability, giving the transformer an edge in tasks requiring nuance and interpretation.

Multiple Perspectives at Once

To enrich this self-attention process, transformers employ what is known as multi-head attention. Rather than relying on a single perspective, the model uses multiple attention mechanisms in parallel, each referred to as a "head." These heads enable the model to capture different types of relationships within the input data. For example, in a sentence like "The quick brown fox jumps over the lazy dog," one head might focus on grammatical structure, another on semantic similarity and another on coreference resolution. By viewing the data through these multiple lenses simultaneously, the model builds a richer and more layered understanding.

This technique is particularly beneficial in complex tasks such as machine translation. When translating a sentence from English to French, one head might specialise in nouns, another in verb conjugation and a third in word order. The outputs of these heads are then linked together and passed through a linear transformation, creating a unified and contextually dense representation. The power of multi-head attention lies in its ability to decompose language into multiple dimensions, ensuring that no single representation becomes a bottleneck. It allows the transformer to generalise better across languages, writing styles and domains of knowledge.

Layer-by-Layer Processing: Feedforward Networks

Once attention has been applied, the output of each attention mechanism is passed through a position-wise feedforward neural network. This feedforward component consists of two linear transformations separated by a non-linear activation function, such as ReLU or GELU. Its role is to process the context-rich output of the attention layers further, transforming it into a more abstract and task-specific representation. Each layer of the transformer, comprising both attention and feedforward sublayers, is wrapped with residual connections and layer normalisation, ensuring stable training and effective gradient flow.

In large transformer models like GPT-4, there are often over a hundred such layers stacked on top of each other. Early layers in the model might be responsible for learning basic grammatical features, identifying named entities or distinguishing sentence types. As we move deeper into the network, the model begins to capture more abstract patterns: reasoning over long passages, inferring intent or recognising subtleties in tone. This layer-wise processing is what enables large language models to handle such a wide range of tasks with consistency and precision. Each successive layer builds on the insights of the previous one, allowing the transformer to simulate something surprisingly close to coherent thought.

Encoders and Decoders

The original transformer model consists of two main parts: the encoder and the decoder. The encoder takes in the full input, like a paragraph or sentence and transforms it into a high-dimensional representation. The decoder then uses this representation to generate an output sequence, such as a translated sentence or a summary.

Encoder-only models like BERT are used for understanding tasks, such as classifying emails as spam or analysing sentiment. Decoder-only models like GPT are designed for generation tasks, like writing emails or coding assistance. Models like T5 and BART use both parts and are flexible across a variety of tasks.

Why Transformers Matter

Who else will defeat the decepticons? But on a serious note, transformers have become a foundational model architecture because they combine scalability, flexibility and performance. Their reliance on parallel processing makes them efficient to train on massive datasets, and their modular structure allows them to be fine-tuned for specific use cases.

The idea of attention, of weighting different parts of the input differently depending on context, is more than a mathematical trick. It mirrors a fundamental aspect of human cognition: the ability to focus, selectively, on what matters. This gives transformers a powerful inductive bias that generalises well across domains.

Conclusion

Transformers represent one of the most important shifts in the history of machine learning. By moving away from recurrence and embracing parallelism and attention, they have enabled a new generation of models that are more powerful, more versatile and more human-like in their understanding of language and patterns.

Understanding how transformers work helps demystify the intelligent systems that increasingly shape our digital experience. Whether you are a developer, a researcher, or simply a curious reader, grasping the core concepts of transformers offers insight into the future of AI and the architecture behind it.

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