How does the Transformer architecture work?
How does the Transformer architecture work?
Ask by ICSM Computer
Updated 27 Apr 2026
The Transformer architecture is a deep learning model introduced in the paper Attention Is All You Need by Ashish Vaswani and colleagues. It is the foundation behind modern AI systems like ChatGPT and models such as BERT and GPT.
At a high level, Transformers process sequences (like text) by focusing on relationships between words, rather than processing them strictly one by one.
Core Idea: Attention Mechanism
The key innovation is self-attention.
Instead of reading a sentence word-by-word like older models (RNNs/LSTMs), a Transformer looks at all words at once and decides:
For example, in:
The word “it” needs context. Self-attention helps the model connect “it” → “cat”.
Main Components of Transformer
1. Input Embedding
Each word is converted into a vector (numbers).
Also, since Transformers don’t inherently understand order, they use positional encoding to add sequence information.
2. Self-Attention (Core Engine)
For each word, the model creates three vectors:
The model computes attention scores using:
This lets each word gather context from the entire sentence.
3. Multi-Head Attention
Instead of one attention calculation, Transformers use multiple attention heads.
Each head learns different relationships:
This makes understanding richer and more nuanced.
4. Feedforward Neural Network
After attention, each token passes through a standard neural network:
5. Add & Normalize
Each layer includes:
Encoder-Decoder Structure
The original Transformer has two parts:
Encoder
Decoder
Generates output step-by-step
Uses:
How It Works Step-by-Step
Pass through multiple layers of:
Why Transformers Are Powerful
Works for:
Real-World Impact
Transformers power:
Models like GPT-4 and T5 are built on this architecture.
In Short
The Transformer works by using self-attention to understand relationships between all parts of input simultaneously, making it far more powerful than earlier sequence models. It replaces sequential processing with context-aware parallel computation, which is why it dominates modern AI.