Q: What is a transformer, and how do transformer models work?

The transformer is the neural network architecture that powers every modern LLM. The name is hiding in plain sight: GPT stands for Generative Pre-trained Transformer. Google researchers introduced the architecture in 2017, in a famous paper called "Attention Is All You Need."

The breakthrough is the attention mechanism. When a transformer processes text, every token looks at every other token and works out which ones matter for its meaning. Take the sentence "the bank was steep and muddy." The word "bank" attends to "steep" and "muddy" and understands it's a riverbank, not a place that holds money. Older architectures read text one word at a time and struggled to hold long-range connections. Transformers see the whole sequence at once.

That "all at once" property had a second effect, and it's the one that changed the industry: transformers can be trained in parallel across thousands of GPUs. That's what made it possible to scale models to billions of parameters, and that scaling is what made modern LLMs possible.

As an AI Engineer, you don't need to build transformers. But knowing how attention works helps you understand why context windows have limits, why long prompts cost more, and why models sometimes lose track of things in the middle of a long conversation.