If you’ve ever chatted with ChatGPT, asked Claude a question, or watched an AI generate an image from text, you’ve witnessed the Transformer algorithm in action. But what actually IS a Transformer? And why did this particular piece of technology change everything?

The Problem: Teaching Computers to Read

Here’s the thing about human language: it’s messy, contextual, and full of nuance. Consider this sentence:

“The cat sat on the mat because it was tired.”

What does “it” refer to? The cat or the mat? You instantly know it’s the cat. After all, mats don’t get tired. But for decades, computers struggled with exactly this kind of reasoning. They processed words one at a time, like reading through a tiny keyhole, unable to see the bigger picture.

Then, in 2017, researchers at Google published a paper with a bold title: “Attention Is All You Need.” [1] That paper introduced the Transformer, and nothing has been the same since.

Step 1: Words Become Numbers

Computers can’t read words. Their natural input is raw numbers. So the first thing a Transformer does is convert each word into a list of numbers, called a “vector” or “embedding.”

Think of it like giving each word GPS coordinates in a vast, multidimensional map of meaning. Words with similar meanings tend to cluster together. “King” is close to “queen,” “happy” is close to “joyful,” and “pizza” is close to “pasta.”

Figure 1: How words get converted into numerical vectors that capture meaning

Step 2: The Magic of Self-Attention

Here’s where the Transformer’s superpower kicks in: self-attention. Instead of reading words one by one, the Transformer looks at ALL words simultaneously and asks a crucial question:

“Which other words should I pay attention to when understanding THIS word?”

Remember the cat sentence mentioned previously? When the Transformer processes the word “it,” self-attention helps it look back at every other word and calculate attention scores. It discovers that “it” should pay HIGH attention to “cat” (because cats get tired) and LOW attention to “mat” (because mats don’t).

Figure 2: Self-attention in action.  “it” learns to focus on “cat”

It’s like being at a party and knowing exactly who to listen to when someone mentions a name. Your brain instantly knows the context. That’s what self-attention gives to computers.

Step 3: Stacking Layers for Deeper Understanding

A single attention layer is clever, but it’s not enough. Modern language models stack many attention layers on top of each other. GPT-3 has 96 layers. GPT-4 has even more.

Each layer builds on the previous one, developing an increasingly sophisticated understanding. Early layers might learn basic grammar. Middle layers understand phrases and idioms. Later layers grasp complex reasoning and world knowledge.

Figure 3: The complete Transformer pipeline: from input to prediction

Why Transformers Changed Everything

Before Transformers, we had other approaches to language AI. RNNs (Recurrent Neural Networks) read text sequentially, like a human reading left to right. The problem? They were slow and forgetful. By the time they reached the end of a long document, they’d forgotten the beginning.

Transformers solved both problems. Because they look at all words simultaneously, they’re parallelizable, meaning they can run on powerful GPUs and process entire documents in one go. And self-attention means they never forget; every word can connect to every other word, no matter how far apart.

This architectural leap enabled the AI revolution we’re living through. ChatGPT, Claude, Gemini, DALL-E, Midjourney, and other LLMs are all built on Transformer foundations.

The Bottom Line

The Transformer isn’t magic, even if it feels that way. It’s an elegant solution to a fundamental problem: how do you teach a machine to understand context? The answer turned out to be: let it look at everything at once, and teach it to pay attention to what matters.

Next time you chat with an AI and it seems to “get” what you’re saying, remember: somewhere in those layers of attention, millions of tiny calculations are figuring out which words matter most to your meaning. It’s not thinking like a human — but it’s doing something remarkable nonetheless.

References

[1] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30, pp. 5998–6008). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html