Understanding the Transformer Model in LLMs

Introduction

When you are using ChatGPT, Google Gemini, Claude or any other modern AI Language Model, you are interacting with a transformer. The transformer architecture introduced in 2017 by Google researchers revolutionized the ability of computers to understand and generate human language. This article will walk you through what transformers are and how they work step-by-step. Before transformers, language models looked at one word at a time. It was like reading a book with a flashlight that only shows one word. This made it hard for the model to understand long sentences. As an Example, In the sentence "The bank was steep," the word "bank" could mean a financial bank or a riverbank. To choose the right meaning, the model needs to see the word "steep". Transformers fix this by looking at all words in a sentence at once. This helps the model see how each word connects to the others

Step 01 - Breaking Text Into Tokens

Before the transformer reads the text, it splits it into smaller units called tokens. Each token becomes a number (a vector) that the model understands. This is called tokenization and embedding.

• Input - "I love artificial intelligence"
• Tokens - ["I", "love", "art", "ificial", "intell", "igence"]

Step 02 - Adding Positional Information

Transformers look at all words at once, so they need to know the order. For example, "dog bites man" is very different from "man bites dog." Positional encoding adds order to the tokens. It's like giving page numbers to mixed-up book pages.

Step 03 - Attention Mechanism

This is the key part of transformers. Attention helps the model focus on important words when reading a sentence. Let's try to understand how Attention Works. Let's assume we need to translate "Le chat noir" (the black cat) into English. For this, the model needs to focus on "Le" and "noir" when working on "chat." The model uses three parts for this

1. Query - What word are we looking at now?
2. Key - What other words are there?
3. Value - What meaning do they give?

The model gives attention scores to all words to decide which ones are more important. Transformers use many attention heads at the same time (Multi-Head Attention). Each head looks at different things like grammar, meaning or logic. This helps the model understand better.

Step 04 - Feed-Forward Neural Networks

After attention, the model sends each word's information through a feed-forward neural network. This helps the model learn more and improve its guesses. It’s like asking, “Now that I understand the sentence better, now what else can I figure out about this word?”

Step 05 - Stacking Layers

Transformers use many layers, not just one. Each layer improves the output of the one before it.

Layer 1 - Finds simple word links
Layer 5 - Understands grammar better
Layer 10 - Handles deep meaning
Layer 20+ - Deals with complex ideas and reasoning

It’s like editing a paper many times to make it a better and clearer output.

Step 07 - Training the Model

Transformers learn by reading lots of text. During training:

• The model reads billions of sentences
• It guesses the next word
• If wrong, it makes small changes to improve
• It repeats this many times

This helps the model learn grammar, facts, patterns, and some logic.

Step 8 - How It Writes Text

When you ask a question:

• Your text becomes tokens
• Tokens go through all the layers
• Attention helps the model find meaning
• It guesses the next word
• Adds it to the answer

This will repeat until the answer is complete

Conclusion

Transformers are more powerful because they can read all words at once, understand long texts well, work better with more data and can be used for many tasks. Transformers changed how AI understands language by focusing on what matters and using many layers of learning. It became the core of most modern language models. They are not magic, but they are clever tools that learn from huge amounts of text and help computers understand and create human language better than ever before.