Transformer in Generative AI
🧠 Transformer in Generative AI — Short Notes ⚙️ How Transformer Works (Step-by-Step) 4 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 👉...