we’ve been talking about generative AI and large language models, but now we’re moving toward the foundation that makes them possible — the transformer architecture. This is part one of the series, and the goal here isn’t to throw heavy math at you. Instead, we’ll just walk through the ideas in plain language.
So, to make things concrete, let’s use a really ordinary sentence:
“Jane threw the Frisbee and her dog fetched it.”
If you’re reading this, your brain probably filled in all the details instantly. Jane is clearly the thrower. The dog is doing the fetching. And when the sentence says “it,” you know “it” refers back to the Frisbee.
For us, that feels so obvious we barely notice the effort. But machines don’t see it the same way. That little “it” isn’t automatically tied to “Frisbee” unless the model can figure out the relationship. And that’s where architecture matters.
RNNs: The First Attempt at Language
Before transformers came along, the main tool for handling sequences of text was the Recurrent Neural Network (RNN).
The basic idea of an RNN is simple enough: it processes one word at a time, while carrying forward a hidden state (basically memory) that gets updated at each step.
Imagine the earlier sentence moving through an RNN:
- First step: it only knows “Jane.”
- Second step: memory updates, now it has “Jane threw.”
- Third step: memory updates again, now “Jane threw and.”
And so on.
At first glance, this seems fine. The model is technically remembering what it’s seen before. But here’s the problem: by the time it gets to “dog” or “it,” the earlier memory of “Frisbee” is already weak or fading.
This memory fading is what people call the vanishing gradient problem. The further back a word is, the harder it becomes for the model to hold on to it. That means RNNs struggle when connections between words stretch across longer sequences.
For short sentences, RNNs do okay. But try giving them a paragraph, or worse, an entire page of text — they’ll miss important links because they can’t keep the bigger picture in mind.
Transformers Flip the Script
Transformers came in and completely changed how we think about processing text.
The big difference is that transformers don’t move word by word in a strict sequence. Instead, they look at the entire sentence at once. Think of it as switching from tunnel vision to a wide-angle view.
So when our example sentence goes in, the transformer doesn’t just hold “Jane” at the start and hope to recall it later. It actually looks at all the words together, figuring out their relationships right away.
That’s why transformers can easily connect “it” back to “Frisbee,” even though the words aren’t side by side.
The Self-Attention Mechanism
The real magic inside transformers is the self-attention mechanism.
Here’s how it works in plain terms: instead of treating all words as equally important, the model learns to weigh some words more heavily depending on context.
So in our case:
- The word “it” is strongly tied to “Frisbee.”
- “dog” is tied to “fetched.”
- “Jane” links strongly with “threw.”
The model pays attention not just to the word itself, but also to how it connects to others around it. And it does this for all words at the same time.
That’s what makes it so different from RNNs. Instead of slowly passing memory along step by step, transformers build a network of relationships across the entire sequence in one go.
Encoder and Decoder: Two Halves of the System
The transformer has two main parts: the encoder and the decoder.
- The encoder takes the input (our sentence, or any text) and encodes it into numerical representations, called vectors. These vectors capture not just the words, but the context and relationships between them.
- The decoder then takes those vectors and produces output — it could be a translated sentence, a summary, or even the next few words in a generated text.
Both the encoder and decoder are made up of layers stacked on top of each other, and each layer includes self-attention. This layering is what allows transformers to refine their understanding as the data passes through.
If that sounds complicated, just remember this: the encoder understands the input, and the decoder uses that understanding to produce output.
Why This Matters
The paper that introduced this — “Attention Is All You Need” — was published in 2017, and it really reshaped the field of AI. Almost every modern language model you’ve heard of, from BERT to GPT, is built on transformers.
The reason is simple: transformers can handle long-range context. They don’t lose track of words the way RNNs do. And they’re also more efficient to train, since they can process multiple words in parallel instead of one at a time.
That’s why they’ve become the backbone for tasks like translation, summarization, question answering, and text generation.
Pulling It Together
Let’s quickly recap:
- Machines struggle with language because meaning depends on connections.
- RNNs tried to solve this by carrying memory forward, but they faded with long sentences.
- Transformers changed the approach by looking at the whole sequence at once.
- Self-attention is the key — it lets the model weigh relationships between words.
- Encoders and decoders form the two halves of the architecture.
That’s the overview for part one.
In the next part, we’ll go deeper into how the encoder-decoder setup actually works, and we’ll look at the finer details of the layers inside transformers.