How Embeddings Are Created | Inside an Embedding Model (GenAI Explained)

Description

How Embeddings Are Created | Inside an Embedding Model Embeddings are the foundation of modern Generative AI systems — powering semantic search, vector databases, and Retrieval Augmented Generation (RAG). In this video, you’ll learn: What embeddings really are and why they matter How text is converted into numerical vectors The role of tokenization and transformer layers How context is captured using attention mechanisms Why embeddings enable semantic similarity search How embeddings are used in RAG and vector databases This video is ideal for: GenAI and LLM practitioners Cloud Architects and ML Engineers AWS and Azure professionals Anyone learning RAG, vector databases, or AI system design 🔍 Topics Covered: Embeddings explained Embedding models Tokenization in LLMs Transformer-based embeddings Semantic similarity Vector space representations Foundations of RAG ▶️ Watch Next: What Is RAG (Retrieval Augmented Generation)? Vector Databases Explained for GenAI RAG vs Fine-Tuning — Choosing the Right Approach Subscribe for more clear, real-world GenAI explanations 🚀