Discover the complete architecture of Generative AI explained through a unique 3-level framework that most creators miss. In this deep dive, we break down how AI systems like ChatGPT, GPT-4, Dall-E 2, and Stable Diffusion actually work—from Foundation Models to real-world Applications. 🔗 Related Playlists : Generative AI Full Course : Langchain Full Course: Official Data Scientist Roadmap : Crack Data Science Interview: 🔍 What You'll Learn: The 3-Level Generative AI Architecture: Model → System → Application How Large Language Models (LLMs) like GPT-4 generate text using transformer architecture Foundation Models explained: Emergence and Consolidation properties Unimodal vs Multimodal AI models and their capabilities Generative Adversarial Networks (GANs) breakdown How Reinforcement Learning from Human Feedback (RLHF) trains ChatGPT Prompt Learning and Prompt Engineering techniques The 4 Critical Limitations: Hallucination, Bias, Copyright, Environmental Impact 🧠 Core Concepts Covered: ✅ Deep Neural Networks and AI algorithms ✅ Generative Modeling vs Discriminative Modeling ✅ Self-attention mechanism in transformers ✅ How AI models learn from training data ✅ Text-to-text, text-to-image, and text-to-music generation ✅ GitHub Copilot, Midjourney, and MusicLM explained ✅ The socio-technical framework of AI systems ⏰ Timestamps: 00:00 - What is Generative AI? 00:42 - 3 Level Framework 00:59 - Level 1 of Gen AI 03:00 - Level 2 of Gen AI 04:26 - Level 3 of Gen AI 05:55 - Core Pillar of Gen AI 06:43 - GAN - New way of Generating 07:25 - Advanced Training Techniques 08:23 - Current Challenges & Future Scope #generativeai #datascience #skdneuron #genai 👉 Don’t forget to like, share & subscribe 🔔 SKD Neuron that breakdown to help others avoid the same pitfalls!