🤖 Large Language Models

How Large Language Models Work: Transformers, Attention, and Tokenization Explained

A practical breakdown of the transformer architecture, attention mechanisms, and tokenization processes that power today's most capable AI language models.

theAIcatchup Apr 24, 2026 5 min read

⚡ Key Takeaways

{'point': 'Transformers process text in parallel', 'detail': 'Unlike older RNN architectures, transformers examine all words simultaneously through self-attention, enabling faster training and better long-range context understanding.'} 𝕏
{'point': 'Attention mechanisms resolve context', 'detail': 'Self-attention computes query-key-value relationships across all tokens, allowing models to understand which words relate to each other regardless of distance in the text.'} 𝕏
{'point': 'Subword tokenization balances vocabulary and flexibility', 'detail': 'BPE and similar algorithms break text into subword units, handling rare words and multiple languages while keeping vocabulary size manageable at around 100,000 tokens.'} 𝕏