---
title: 'How ChatGPT Transformers Work? | ChatGPT Tokenization & Architecture Explained Simply | Simplilearn'
source: 'https://youtube.com/watch?v=i2Dc41fNt7c'
video_id: 'i2Dc41fNt7c'
date: 2026-10-26
duration_sec: 69
channel: 'Simplilearn'
---

# How ChatGPT Transformers Work? | ChatGPT Tokenization & Architecture Explained Simply | Simplilearn

> Source: [How ChatGPT Transformers Work? | ChatGPT Tokenization & Architecture Explained Simply | Simplilearn](https://youtube.com/watch?v=i2Dc41fNt7c)

## Summary

The video explains how transformer models power ChatGPT, from processing entire sentences simultaneously to using attention mechanisms and embeddings. It traces the journey of input text through multiple layers of understanding to predict and generate responses word by word.

### Key Points

- **Transformers process whole sentences at once** [00:03] — A transformer analyzes the entire sentence at once, unlike older AI systems that read word by word, allowing better context understanding.
- **Attention identifies key words** [00:17] — The attention mechanism helps the model decide which words are most important when predicting the next word.
- **Words become embeddings** [00:17] — Each word gets converted into numbers called embeddings, enabling the computer to understand relationships between words.
- **Layered processing builds understanding** [00:31] — Embeddings pass through multiple transformer layers where the model repeatedly compares words, finds patterns, and builds deeper understanding.
- **Sequential word prediction** [00:44] — Using training data, the model predicts the single most likely next word and repeats this process thousands of times per second to generate a complete response.

## Transcript

a transformer model starts working behind the scenes in just a fraction of a second. Unlike older AI systems that read one word at a time, a transformer looks at the entire sentence together to understand the complete context. Its
attention, which helps the model decide which words are most important when predicting the next word. Each word is first converted into numbers called embeddings, allowing the computer to understand relationships between
embeddings pass through multiple transformer layers where the model repeatedly compares words, finds patterns, and builds a deeper understanding of the sentence. Using everything it has learned during
training, the model predicts the single most likely next word. It repeats this prediction thousands of times every second until the complete response is generated. This ability to understand context and generate text one word at a
time is what makes modern AI models like chat GPT feel surprisingly intelligent. Subscribe to this channel for more such information.