Transformer Architecture
The Committee That Reads the Whole Room
9 min read
The Committee That Reads the Whole Room
A committee where every member reads every other member's notes before voting — not just the person next to them.
Old AI read text like a student cramming — word by word, left to right, forgetting the beginning by the time it reached the end. The Transformer changed this: every word pays "attention" to every other word simultaneously. "Bank" next to "river" vs "bank" next to "money" — the Transformer knows the difference by reading the whole sentence at once.
In Plain English
The Transformer is the architecture underneath almost every modern AI model. Its key innovation is "attention" — every word in a sentence looks at every other word to understand context, rather than reading left to right like older models.
The Technical Picture
Transformers use self-attention mechanisms where each token computes query, key, and value vectors. Dot-product attention scores determine how much each token attends to every other token in the sequence, enabling parallelisation and long-range dependency capture that RNNs could not achieve.
Real-World Examples
- GPT, Claude, Gemini, and Llama are all Transformer-based models
- The original paper 'Attention Is All You Need' (2017) by Google Brain sparked the AI revolution
- Vision Transformers (ViT) extended the architecture to images
The Transformer's attention mechanism is the single most important innovation in modern AI — it's the engine under LLMs, diffusion models, and multimodal AI.