Transformer Architecture

Why You Need to Understand Transformer

Almost all modern large language models (GPT, Claude, LLaMA, etc.) are based on the Transformer architecture.

Understanding Transformer doesn't require deep math — just understanding its core idea: enabling the model to "see" all words in a sentence when processing each word, not just the preceding ones.

This solved the fundamental problem with traditional models (like RNNs) — inability to effectively handle long-range dependencies.

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What Is Transformer

One-line definition: Transformer is a deep learning architecture proposed by Google in 2017 that uses "Self-Attention Mechanism" to enable models to simultaneously process relationships between any positions in a sequence.

Core mechanism: Self-Attention

• When processing each word, calculate its relevance to all other words in the sentence
• "Today" and "weather" have high relevance, "today" and "rocket" have low relevance
• This "who is related to whom" calculation lets the model understand context

Analogy: Like reading — you don't stare at just the current word, but your gaze goes across the entire sentence or even paragraph, understanding each word's meaning in context.

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How to Do It: Practical Significance for Users

As a user, you don't need to care about the underlying attention calculations, but you need to understand the capabilities it brings:

Stronger context understanding: models can utilize information across the entire window, not just recent sentences. This leads to the concept of Context Window — Transformer made expanding Context Window possible (from 4K to 128K Tokens).

Longer context windows: this directly affects how much external knowledge can be拼入 RAG systems, and how much history Memory can retain.

Scaling Law: larger model scale leads to continuous performance improvement — making "brute force works" possible.

Pretraining + Fine-tuning paradigm: after large-scale pretraining, adapting to multiple tasks with a small amount of fine-tuning data.

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Remember this: Transformer lets models "see" the entire context, not just the preceding words — this is the foundation for modern LLMs to understand long texts and perform complex reasoning.

Related terms: Context Window · Embedding