What Is a Transformer in AI?
The transformer is the neural network architecture that powers every modern large language model — including GPT, Claude, Gemini, and Llama.
The Key Innovation: Attention
Introduced in the 2017 paper “Attention Is All You Need,” the transformer replaced older sequential architectures (RNNs, LSTMs) with a mechanism called self-attention.
Self-attention lets the model look at all words in a sentence simultaneously and learn which words are related to each other, regardless of distance:
“The cat sat on the mat because it was tired.”
A transformer understands that “it” refers to “the cat” — not “the mat” — by computing attention scores between all word pairs.
Why Transformers Won
- Parallelization: Unlike RNNs, transformers process all tokens at once, making them much faster to train on GPUs.
- Scaling: Performance improves predictably as you add more parameters and data.
- Versatility: The same architecture works for text, code, images, audio, and video.
Transformer Variants
| Type | Used For | Examples |
|---|---|---|
| Decoder-only | Text generation | GPT, Claude, Llama |
| Encoder-only | Text understanding | BERT, RoBERTa |
| Encoder-decoder | Translation, summarization | T5, BART |
Modern LLMs are almost exclusively decoder-only transformers — trained to predict the next token in a sequence.
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