This chapter demystifies the Transformer—the architecture underpinning today’s large language models—by breaking it down into its four core components: embeddings, encoder, decoder, and output layer. We begin with Byte-Pair Encoding, a subword tokenization scheme that balances vocabulary size and sequence length, then dissect the dual embedding layer that combines token and positional vectors. Next, we unpack the self-attention mechanism—multi-head attention with masking—to show how it replaces recurrence with parallelizable, context-aware weighting of all tokens. Building on Add & Norm and position-wise feed-forward sublayers, we assemble complete encoder and decoder stacks and discuss their training (with teacher forcing) and inference (token-by-token decoding). Finally, through a hands-on text-to-SQL case study, we piece together end-to-end training, evaluation with metrics such as perplexity and BLEU, and real-time query generation. This equips readers to implement and adapt Transformers for advanced language tasks.