Transformer Architecture Basics
Transformers are the neural network architecture behind every modern LLM. Self-attention lets the model weigh how relevant each word is to every other word — enabling long-range understanding.
TL;DR: Transformers are the neural network architecture behind every modern LLM. Self-attention lets the model weigh how relevant each word is to every other word — enabling long-range understanding.
Why Transformers Replaced RNNs
Before transformers (2017), language models used Recurrent Neural Networks which processed tokens one-by-one, losing context over long sequences. Transformers process all tokens simultaneously and use attention to relate distant words directly.
Self-Attention: The Key Mechanism
"Attention is All You Need" (2017) introduced self-attention: for each token, the model computes how much it should "attend to" every other token. This creates context-sensitive representations — the word "bank" means something different near "river" vs "money".
Scale is the Secret Sauce
GPT-3 has 175B parameters; GPT-4 is estimated at ~1.8T in a mixture-of-experts design. More parameters + more data + more compute = dramatically better performance. This "scaling law" is why LLMs keep getting better with size.
What This Means for AI Tools
Context window limits come from the quadratic complexity of attention (longer = exponentially more compute). Model quality differences (GPT-4 vs GPT-3.5) come from architecture improvements and training scale. Smaller models (GPT-4o-mini) sacrifice some quality for speed and cost.