AI Engineering for Devs: Module 1 / Lesson 1

Description

Module 1 / Lesson 1 Overview: 00:00:00 - Welcome & Course Framing 00:00:52 - Foundations: Why Vector Stores? 00:02:57 - What is a Vector Store? 00:04:53 - Embeddings: Turning Meaning into Math 00:06:53 - Tokenization: The Model’s “Alphabet” 00:12:30 - Visualizing Embeddings: Similarity and Clusters 00:14:50 - Vector Math: Meaningful Arithmetic 00:17:36 - Choosing Embedding Dimensions 00:22:06 - Embedding Model Types & Training 00:26:45 - Picking an Embedding Model 00:42:26 - Embeddings Across Modalities 00:44:17 - Semantic Similarity & Search in Vector Stores 00:46:22 - Beyond Semantics: Other Search Types 00:49:09 - Types of Search & Retrieval 00:54:46 - Indexing Methods in Vector Stores 00:58:51 - Vector Store Options: Comparing Products 01:00:06 - Why we chose PGVector 01:03:00 - Recap & Next Steps