A Transformer is a neural network architecture built around attention. Instead of processing text strictly one step at a time, a Transformer can look across many parts of the input and learn which pieces matter most to each other. That ability made it dramatically better than many older sequence models for language tasks and helped make large language models practical.
What Makes Transformers Different
The key idea is self-attention. When the model reads a sequence, it learns how strongly each token should attend to the others. That helps it capture long-range relationships such as references, topic changes, code structure, or sentence meaning. Combined with many stacked layers, attention lets the model build increasingly rich internal representations of the input.
Transformers also scale well. Because much of the input can be processed in parallel, they are well suited to large training runs on modern hardware. That scaling property helped drive the rise of today's large language models, which rely on Transformers to learn from massive corpora of text.
Where Transformers Are Used
Transformers are no longer limited to text. They are now used in computer vision, speech, code generation, recommendation systems, and multimodal learning. In many systems, the same broad architectural idea can handle words, image patches, audio segments, or other sequences once those inputs are converted into embeddings.
That does not mean every AI model is a Transformer, or that Transformers solve every problem equally well. They can be expensive to train and serve, and long context handling still comes with trade-offs in cost and reliability. But if someone wants to understand modern AI, understanding Transformers is one of the best places to start.
Related concepts: Large Language Model (LLM), Tokenization, Context Window, Embedding, and Multimodal Learning.