Learn · The Inference Pipeline · How Generation Works
The Transformer Architecture
In plain English
Transformers are like a room full of experts who talk to each other to figure out the context of a sentence! 🗣️ They pay 'attention' to all words at once.
What is actually going on
The Transformer relies on multi-head self-attention mechanisms to weigh the relevance of tokens against each other, allowing for robust modeling of long-range dependencies compared to legacy RNNs.
Why it matters
The O(N²) complexity of attention is why long context windows require massive amounts of memory and compute.
Another way to think about it
Reading a book by looking at the whole page at once, instead of scanning line-by-line. 📖
Check yourself
What key mechanism defines the Transformer architecture?
- Recurrence
- Self-Attention
- Convolution
- Markov Chains
Show answer
Self-Attention — Self-attention allows the model to map dependencies across all tokens simultaneously.
Transformers read text sequentially, strictly from left to right like older RNNs.
- True
- False
Show answer
False — Transformers process tokens in parallel during the prefill phase, looking at the entire sequence at once.
Who introduced the Transformer architecture in 2017?
- Apple
- OpenAI
- Meta
Show answer
Google — Google introduced it in the famous paper 'Attention Is All You Need'.