How Transformers Actually Work: The Block Behind Every LLM #Shorts

How does a model turn 'the cat sat on the ___' into 'mat'? It looks like magic, but inside it's just one simple block, repeated over and over: the transformer, the engine behind every large language model. A transformer is a tall stack of identical layers. Your text comes in as tokens, each token becomes a vector (a list of numbers), and those vectors flow up through the stack - each layer refining the meaning - until the top produces a prediction for the next token. Each block does two things: (1) attention - every token looks at the tokens before it and pulls in the context that matters (who did what to whom); (2) a feed-forward network - each token is processed on its own to recall facts and patterns. Both are wrapped with a residual connection (a shortcut) and a normalization step so the signal flows cleanly through a very deep stack. Repeat the block dozens of times, and a final layer turns the top vector into probabilities over the whole vocabulary - pick one, and that's your next token. The limits fall out of the design: it predicts one token at a time (autoregressive), attention cost grows with the square of the length (so context windows are capped), and reasoning isn't built in - it emerges from scale. GPT, Claude, Gemini, and Llama are all decoder-only transformers - the same block scaled to billions of parameters - from the 2017 paper 'Attention Is All You Need'. Full lesson (11 free, no signup): https://systemdesign.academy/go/transformer #Transformers #LLM #AI #DeepLearning #Attention #AIEngineering #MachineLearning #SystemDesign #Shorts