LLM Architecture : The Transformer

Many people use LLMs every day, but fewer understand what actually happens inside the model. This whiteboard breaks down the Transformer (decoder-only) architecture in a clear and practical way. It starts with input text, which is first converted into tokens. These tokens are mapped into a vector space using embeddings, and positional encoding is added so the model understands word order. The core of the model is multi-head self-attention. This is where the model decides which words matter most when predicting the next token.

Figure : LLM Architecture : The Transformer

In simple terms, attention allows the model to focus on relevant context, not just nearby words. Each transformer block includes:

1. Self-attention: Helps the model decide which words to focus on by looking at the entire sentence at once.

2. Residual connections: Allow information to flow directly across layers, making learning more stable and preventing loss of important details.

3. Add & Norm: Combines the old and new information, then normalizes it to keep the model balanced and stable during training.

4. Feed-forward network: Applies non-linear transformations to each word independently, helping the model learn deeper patterns.

These components help the model remain stable while learning long-range relationships. Finally, the output passes through linear layers and softmax to produce probabilities over the vocabulary, enabling the model to predict the next word. This architecture is the foundation behind modern systems like ChatGPT, code assistants, and AI agents. Understanding this flow makes it easier to reason about prompting, fine-tuning, and context engineering.