Module 8: Vector Embeddings Simplified

Understand vector embeddings, similarity, cosine distance, and vector databases in plain English. Pick the right embedding model and vector DB for your project.

Module 8: Vector Embeddings Simplified

What this module gives you

Get fluent with vectors, embeddings, similarity, and the vector database choices you will face in Module 9. By the end, you can compare, ingest, and query embeddings confidently.

Skills you will pick up

Explaining embeddings without math
Computing cosine similarity
Picking an embedding model
Choosing a vector DB
Running your first embedding queries

Why it matters in production

Embeddings power RAG, semantic search, recommendations, classification, and clustering. Picking the right embedding model often improves a feature more than picking a fancier LLM.

Lessons in this module

Recap

Embeddings, cosine similarity, model choices, vector DBs, and your first semantic search are now in your toolkit. You are ready to build a real RAG system.

Ready to start?

Open Lesson 8.1: What is an embedding, really?

Start first lesson