In this article, we will look at how the transformer architecture works in a step-by-step manner.

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Transformers are the core architecture powering modern large language models like GPT and Claude. The architecture consists of three main components: an embedding layer that converts text tokens into numerical vectors, multiple transformer layers that use attention mechanisms to understand word relationships and context, and an output layer that converts results back to text. The process works through iterative cycles: text is tokenized, converted to embeddings with positional information, processed through stacked transformer layers where attention mechanisms weigh the importance of different words, and finally converted back to text through probability sampling. Each transformer layer learns different patterns—early layers capture grammar, middle layers understand sentence structure, and deep layers extract abstract meaning. During training, the model learns from billions of text examples by adjusting weights, while during inference it uses frozen weights to generate responses without learning new information.

How Transformers Architecture Powers Modern LLMs

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Step 4: The Attention Mechanism in Transformer Layers

Training Versus Inference: Two Different Modes