The Transformer: The Architecture That Changed Everything

In 2017, the paper "Attention Is All You Need" was published by researchers at Google. It introduced the Transformer architecture. Before this paper, NLP was slow and struggled with long sentences. After this paper, the path to GPT-4, Claude, and modern AI agents was cleared.

For an LLM Engineer, the Transformer is the "Engine" under the hood. Understanding its components—the Encoder and the Decoder—will help you understand the architectural choices of different models (e.g., why Claude is different from BERT).

1. Why Transformers Won: Parallelization

Before Transformers, we used Recurrent Neural Networks (RNNs). RNNs processed words one by one (linearly). To understand the 10th word, the model had to process words 1 through 9 first.

Transformers process ALL words in a sequence simultaneously.

This allowed researchers to use massive clusters of GPUs to train on the entire internet at once. This speed shift from "Sequential" to "Parallel" is what allowed the "Large" in Large Language Models.

2. The Two Halves: Encoder and Decoder

The original Transformer was designed for translation (e.g., English to French) and had two parts:

The Encoder (The "Understander")

Job: Analyzes the input text and creates a rich mathematical representation of it.
Example Model: BERT.
Strength: Excellent at understanding context, sentiment analysis, and classification.

The Decoder (The "Generator")

Job: Takes a representation and generates a new sequence of tokens, one by one.
Example Models: GPT (Generative Pre-trained Transformer), Claude, Llama.
Strength: Predicting the next word, creative writing, and reasoning.

graph TD
    A[Input Text] --> B[Encoder: Rich Context]
    B --> C[Bottleneck/Vector]
    C --> D[Decoder: Generate Response]
    D --> E[Output Text]
    
    style B fill:#e1f5fe,stroke:#01579b
    style D fill:#fff9c4,stroke:#fbc02d

Note: Most modern LLMs (like GPT-4) are Decoder-only architectures. They have integrated the "understanding" part directly into the generation process.

3. Key Components of the Transformer

There are three architectural innovations you must know:

A. Positional Encoding

Since Transformers process all words at once, they lose the sense of "order." To a Transformer, "Dog bites man" and "Man bites dog" look identical without help. Positional Encoding adds a unique mathematical signature to each word's embedding to indicate its position in the sentence.

B. Multi-Head Attention

Instead of focusing on just one relationship between words, the Transformer uses "Multiple Heads" to look at different relationships simultaneously.

Head 1: Focuses on grammar (Subject-Verb).
Head 2: Focuses on entities (Names/Places).
Head 3: Focuses on tone (Formal/Informal).

C. The Feed-Forward Network

After the Attention layers have "gathered" the information, the Feed-Forward network "processes" it and prepared it for the next layer.

4. Why This Matters for LLM Engineers

Context Window Limits

A Transformer's memory (Attention) is technically $O(n^2)$. This means as the input text doubles, the computational cost quadruples. This is why context windows have limits and why "long context" models (like Gemini 1.5 Pro) use engineered tricks like FlashAttention or Ring Attention.

Temperature and Top-P

Generation happens in the Decoder. Because the output is a probability distribution over the entire vocabulary, we use parameters to control the randomness:

Temperature: Higher = more random jumps between low-probability words.
Top-P: Limits the model to only the "total probability" of the top words.

Visualizing the Flow

sequenceDiagram
    participant User as User (Text)
    participant Token as Tokenizer
    participant Embed as Embeddings + Positional
    participant Attn as Attention Layers
    participant Gen as Next-Token Prediction
    User->>Token: "Hello, how are..."
    Token->>Embed: [1549, 703, ...]
    Embed->>Attn: Semantic Vectors
    Attn->>Attn: Relationship Analysis
    Attn->>Gen: Probability: "you" (98%)
    Gen->>User: "you"

Summary

RNNs (Old): One word at a time. Slow. Forgot things.
Transformers (New): All words at once. Massive scale. Perfect focus.
Encoders: Good for understanding.
Decoders: Good for generating (LLMs).

In the next module, we will move from theoretical math to practical implementation as we explore Python for LLM Engineering.

Exercise: Architect's Choice

If you were building a system to detect hate speech in a social media feed, would you choose an Encoder-based model (like BERT) or a Decoder-based model (like GPT)?

Hint: Do you need to generate new text, or just classify existing text with high semantic accuracy? Look at the strengths of each architecture above.