Transformers Explained for Software Engineers
The article explains the Transformer architecture and attention mechanism for software engineers, breaking down key concepts like self-attention, multi-head attention, and the encoder-decoder structure that underpin modern large language models.
Background
The Transformer is the neural-network architecture that underpins nearly every modern large language model (LLM) — GPT, Claude, Gemini, Llama, etc. It was introduced by Google researchers in a 2017 paper titled "Attention Is All You Need." Unlike earlier sequence models (like RNNs or LSTMs), the Transformer processes all words in a sentence in parallel rather than one-by-one, making it far more scalable. Its key innovation is the "attention mechanism," which lets the model weigh how much each word (or token) should "pay attention to" every other word when computing a representation. "Self-attention" is when a word attends to other words in the same sentence; "cross-attention" involves attending to a separate input (e.g., a prompt). The architecture has two main blocks: an encoder (which reads the input) and a decoder (which generates output). Most modern LLMs use either the decoder-only variant (e.g., GPT) or the encoder-decoder form (e.g., T5). Understanding the Transformer is essential for anyone working with or building on LLMs.