What Is a Transformer in AI?

A Transformer is a neural network architecture designed to process sequential data, particularly text, by understanding the relationships between different elements of a sequence.

Introduced in the 2017 paper Attention Is All You Need, the Transformer became the foundation of modern Large Language Models (LLMs) such as GPT, Claude, and Gemini.

Core Idea

The key innovation of the Transformer is self-attention.

Instead of processing a sentence strictly from left to right, the model can look at all tokens in a sequence and determine which ones are important to each other.

For example:

"The cat sat on the mat because it was tired."

             ┌───────────┐
"it" ───────►│ Attention │──────► "cat"
             └───────────┘
                    │
                    ▼
          Understand the context

This allows the model to capture relationships between words, even when they are far apart.

Main Components

A Transformer is built from several important components:

1. Tokenization

Text is converted into smaller units called tokens.

"Hello world"
      ↓
["Hello", " world"]
      ↓
[15496, 995]

2. Embeddings

Tokens are converted into numerical vectors called embeddings.

Token → Vector

"cat" → [0.21, -0.84, 0.37, ...]

These vectors represent semantic information that the neural network can process.

3. Positional Encoding

Because Transformers process tokens in parallel, they need information about the position of each token in the sequence.

The   cat   eats   fish
 0     1      2      3

4. Self-Attention

Self-attention determines how much each token should pay attention to the other tokens.

It is commonly described using three matrices:

• Query (Q) — what the token is looking for
• Key (K) — what each token offers
• Value (V) — the information associated with each token

The attention mechanism is approximately:

Attention(Q, K, V)
    = softmax(QKᵀ / √dₖ)V

5. Multi-Head Attention

Instead of having a single attention mechanism, Transformers use multiple attention heads.

Each head can learn different relationships:

              ┌─ Head 1 → syntax
Input ────────┼─ Head 2 → semantics
              ├─ Head 3 → context
              └─ Head 4 → relationships

The results are then combined.

6. Feed-Forward Network

After attention, each token passes through a small neural network that further transforms its representation.

7. Residual Connections & Layer Normalization

Transformers use residual connections and layer normalization to make training deep networks more stable.

Transformer Architecture

A simplified Transformer block looks like this:

Input
  │
  ▼
Multi-Head Self-Attention
  │
  ▼
Add & Normalize
  │
  ▼
Feed-Forward Network
  │
  ▼
Add & Normalize
  │
  ▼
Output

Multiple blocks can then be stacked together to create a large Transformer model.

Encoder vs Decoder

The original Transformer architecture contains two parts:

Input
  │
  ▼
┌─────────────┐
│   Encoder   │
└─────────────┘
       │
       ▼
┌─────────────┐
│   Decoder   │
└─────────────┘
       │
       ▼
    Output

Different modern models use different configurations:

• Encoder-only — useful for understanding text, e.g. BERT.
• Decoder-only — commonly used for text generation, e.g. GPT.
• Encoder-decoder — useful for tasks such as translation, e.g. T5.

Why Transformers Matter

Transformers replaced many recurrent architectures such as RNNs and LSTMs for large-scale language modeling because they can process sequences much more efficiently in parallel during training.

Their ability to scale with large datasets and large numbers of parameters made them the foundation of modern AI systems.

Transformers and LLMs

Most modern LLMs are based on the decoder-only Transformer architecture.

A simplified generation process looks like:

Prompt
  │
  ▼
Tokenization
  │
  ▼
Embeddings
  │
  ▼
Transformer Blocks
  │
  ▼
Next-token probabilities
  │
  ▼
Selected token
  │
  └──────────────┐
                 ▼
          Generate next token

The model repeatedly predicts the next token until the response is complete.