What You Will Learn

In this article, you will learn:

• Why transformers became important.
• What self-attention means.
• How tokens, embeddings, and positional encoding fit together.
• The difference between encoder, decoder, and decoder-only models.
• Why transformers power modern LLMs.

Introduction

Transformers are the deep learning architecture behind many modern AI systems, including large language models.

Before transformers, sequence models such as RNNs and LSTMs processed text step by step. Transformers can look at many parts of the input at once and learn which parts matter most.

The Core Idea

The transformer asks:

Which words or tokens should this token pay attention to?

Example:

The bank approved the loan because it passed risk checks.

The word it likely refers to loan, not bank.

Self-attention helps the model learn this relationship.

Transformer Flow

flowchart TD
    A["Input text"] --> B["Tokens"]
    B --> C["Embeddings"]
    C --> D["Positional encoding"]
    D --> E["Self-attention"]
    E --> F["Feed-forward network"]
    F --> G["Output representation"]

Tokens

Models do not directly read words. They read tokens.

Tokens can be:

• Full words.
• Parts of words.
• Punctuation.
• Spaces or special markers.

Embeddings

Each token is converted into a numeric vector called an embedding.

Embeddings capture meaning in vector form.

token -> embedding vector

Positional Encoding

Transformers process tokens in parallel, so they need a way to know token order.

Positional encoding adds order information.

Example:

dog bites man
man bites dog

The same words have different meanings because order matters.

Self-Attention

Self-attention lets each token compare itself with other tokens in the same input.

It calculates attention scores that represent importance.

Current token + other tokens = attention weights

Multi-Head Attention

Multi-head attention runs multiple attention calculations in parallel.

Each head can focus on different relationships:

• Grammar.
• Entity references.
• Topic.
• Sequence order.
• Important facts.

Encoder, Decoder, and Decoder-Only Models

Architecture	Used For
Encoder	Understanding input text
Decoder	Generating output text
Encoder-decoder	Translation and text-to-text tasks
Decoder-only	Most chat and completion LLMs

Why Transformers Work Well

Transformers are powerful because they:

• Learn long-range relationships.
• Process tokens efficiently.
• Scale well with large datasets and compute.
• Support pretraining and fine-tuning.
• Work across text, code, images, and audio.

Interview Questions

What is self-attention?

Self-attention is a mechanism that lets each token decide which other tokens in the input are most relevant.

Why do transformers need positional encoding?

Because transformers process tokens in parallel and need extra information about token order.

Why are transformers important for LLMs?

They scale well, learn context effectively, and generate fluent text by modeling relationships between tokens.

Summary

Transformer architecture combines tokens, embeddings, positional information, self-attention, and feed-forward layers. This architecture is the foundation for modern LLMs and Generative AI systems.