Claude's core architecture is the Transformer, which understands language through the "Attention mechanism." Attention lets the model simultaneously reference the entire input sequence when processing each Token — not just the preceding few words. This enables Claude to understand that "bank" means different things in different contexts, track which noun "it" refers to, and connect background information at the beginning of a document with a question at the end.
The Transformer architecture was introduced in 2017 by Google's research paper "Attention is All You Need," fundamentally transforming natural language processing. Before Transformers, language models (like LSTMs) processed text sequentially, causing poor long-text handling efficiency and difficulty capturing long-range dependencies. Transformer's parallel processing capabilities and Attention mechanism, enabling simultaneous processing of entire input sequences, also made large-scale training expansion possible — ultimately giving birth to large language models like GPT and Claude.
Understanding Claude's underlying architecture has several direct practical impacts. First, you'll understand why repeating or emphasizing certain information works: the Attention mechanism gives higher attention to Tokens that appear frequently or in key positions. Second, you'll understand hallucination's source: when the Attention mechanism can't find sufficient "reference points" in training data, the model outputs the highest-probability but potentially inaccurate Token. Third, you'll understand why Context Window size matters: Attention calculation operates across the entire input sequence, so the larger the Context Window, the more information Claude can "see" and integrate.
Translate Transformer and Attention understanding into practical usage techniques: put your most critical instructions in the first paragraph of your Prompt, don't leave them for the end; if your task requires Claude to pay special attention to a specific section, say so explicitly rather than expecting automatic identification; in long conversations, if Claude starts "forgetting" important information from earlier, re-state it directly in your new message; understanding the Token concept helps you estimate costs — Chinese characters are approximately 1-2 tokens each, English words approximately 0.75 tokens each.