LLMs & RAG Explained: From Self‑Attention to Vector Databases (Full Masterclass)

Ever wondered what really happens inside ChatGPT when you hit Enter? In this video, we strip away the hype and break down the engine behind modern AI — the Transformer architecture that powers models like GPT‑4, Claude, and Gemini. We walk through the legendary “Attention Is All You Need” paper in plain English, then connect it to how Large Language Models work today — including embeddings, self‑attention, and how RAG (Retrieval Augmented Generation) is solving the hallucination problem. This isn’t surface‑level AI talk. This is how the system actually works. 🧠 In this video, you’ll learn: 🚀 Why Transformers replaced RNNs and LSTMs 🧩 How Self‑Attention lets AI understand context and meaning 📐 The “Word Math” behind Vector Embeddings 🔗 How RAG connects LLMs to real‑time, factual data 🔮 What scaling unlocks — and why emergent abilities appear Whether you’re an aspiring AI engineer, a developer, or just deeply curious about the tech shaping the future, this deep dive is for you. 📚 Resources: 🔗 Slides: https://docs.google.com/presentation/d/1e-xCizgO42acjvxSJhLe6krZ8R1RpFOAT-rszYTkimw/edit?slide=id.p1#slide=id.p1 🔗 Code & Examples: htttps://github.com/tincharlie/