The neural network architecture behind modern AI — introduced by Google in 2017 and now powers ChatGPT, Claude, and most other LLMs.
Transformers are the neural network design that made modern AI possible. Before transformers (2017), AI models processed language word-by-word sequentially. Transformers introduced 'attention' — the ability to look at all words in a sentence simultaneously and decide which words matter most for understanding each other word. This made models dramatically better at language and much faster to train.
Imagine reading a sentence word-by-word with no ability to look back — you'd forget what you just read. Older models worked like this. Transformers are like reading a sentence and being able to consider how every word relates to every other word at the same time. 'The cat sat on the mat because it was tired' — transformers can instantly connect 'it' to 'cat' by weighing the attention across all words at once.
The transformer architecture, introduced in 'Attention Is All You Need' (Vaswani et al., 2017), replaces recurrent architectures (RNNs, LSTMs) with self-attention mechanisms. Core components: multi-head self-attention layers that compute relationships between all input tokens simultaneously, position embeddings that preserve sequence order, feed-forward networks, and layer normalization. Key advantages: parallelizable training (unlike sequential RNNs), ability to capture long-range dependencies, and scalability to very large models. Modern variants include decoder-only (GPT family), encoder-only (BERT), and encoder-decoder (T5) architectures.
OpenAI's GPT family (including ChatGPT) use decoder-only transformer architecture.
Google's BERT, designed for understanding tasks, uses encoder-only transformers.
Anthropic and Google's flagship models are transformer-based with various architectural improvements.
The transformer idea applied to images — breaking images into patches treated like tokens.
The name comes from the paper's focus on the model's ability to 'transform' input sequences into output sequences using attention mechanisms. It has nothing to do with the movie franchise! The name has become iconic in AI, even if it's a bit unusual.
No, but most modern language and multimodal models are. Other architectures exist: RNNs and LSTMs for simpler sequence tasks, CNNs for images (though Vision Transformers are overtaking them), and newer architectures like Mamba that aim to be more efficient. However, for the most capable AI systems in 2026, transformers remain dominant.
Three reasons: (1) They could be trained in parallel, making it practical to scale to billions of parameters. (2) Self-attention captured relationships across long passages that RNNs couldn't. (3) The architecture turned out to be remarkably general — the same transformer works for language, images, audio, and code. Before transformers, each domain needed custom architectures; after, one architecture dominates.
A neural network trained on massive text data to understand and generate human-like language.
✍️The skill of writing instructions to AI models to get the best possible output.
🔍A technique that lets AI models look up information before answering, improving accuracy and reducing hallucinations.
🎯The process of further training a pre-trained AI model on specific data to specialize its behavior for a particular task or domain.
Our free AI course teaches you to use these ideas in real projects.
Start Free AI Course →