Transformer in Generative AI
🧠 Transformer in Generative AI — Short Notes
⚙️ How Transformer Works (Step-by-Step)
1. Input Embedding
- Converts words into numerical vectors
- Example: “AI” → [0.21, 0.78, …]
2. Positional Encoding
- Adds word order information
- Important because transformer reads all words at once
3. Self-Attention Layer
- Finds relationships between words
- Helps understand context
👉 Example:
“The cat drank milk because it was hungry”
→ “it” refers to cat
4. Feed Forward Network
- Fully connected neural network
- Performs deep feature processing
5. Output Prediction
- Predicts next word/token
- Builds sentence step-by-step
🔄 Types of Transformers in Generative AI
| Type | Example | Use |
|---|---|---|
| Decoder-only | GPT | Text generation (Chatbots, AI writing) |
| Encoder-only | BERT | Text understanding (search, classification) |
| Encoder-Decoder | T5 | Translation, summarization |
🚀 Key Points to Remember
- Transformer is based on 👉 Attention Mechanism
- Processes entire sentence in parallel
- Much faster than RNN/LSTM
- Backbone of modern AI like
👉 ChatGPT
🧩 Quick Revision Trick (1 Line)
👉 Embedding → Position → Attention → Processing → Prediction
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