Transformer, explained
A transformer is the type of neural network architecture that underlies virtually all modern AI language models — it's the design that made models like GPT, Claude, and Gemini possible.
Before transformers, AI models processed text word by word in sequence, which made it hard to relate words that were far apart in a sentence and slow to train on large datasets. The transformer architecture, introduced in a 2017 research paper, solved both problems with a mechanism called 'self-attention' — a way for the model to weigh the relationship between every word and every other word in a passage at the same time.
Self-attention is what lets a transformer understand that in 'The bank by the river flooded, so we moved the cash to higher ground,' the word 'bank' refers to a riverbank, not a financial institution — because the surrounding words all get weighed together. This ability to track long-range context at scale is why transformers enabled such a dramatic leap in language model quality.
You don't need to understand transformer internals to use AI effectively, but knowing the term helps when you encounter it in model documentation, research summaries, or product comparisons. When someone says 'transformer-based model,' they're describing the overwhelming majority of commercially available AI language tools today.
Go deeper
Wield's AI Foundations track covers this hands-on, in plain English, with real examples and a copy-paste prompt to try it yourself.
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