Vector Databases Explained — learn how modern AI systems search by meaning, not just keywords. This is Phase 5 (Eval-Driven RAG Systems) of the AI Engineer 360 Course.

An AI model without a Vector Database is like Google without Search. It may generate answers, but it can't retrieve the right knowledge from millions of documents.

In this video, you'll understand the core technology behind every modern RAG system, including ChromaDB, Pinecone, Embeddings, Semantic Search, and Metadata Filtering.

What You'll Learn
What is a Vector Database?
Why SQL Databases aren't enough for AI
Embeddings
How Vector Search Works
ChromaDB Explained
Pinecone Explained
ChromaDB vs Pinecone
Metadata Filtering
Enterprise AI Use Cases

If you want to build production-ready AI Assistants, Enterprise Search, PDF Chatbots, and RAG Systems, mastering Vector Databases is an essential skill.

Subscribe to The Techzeen and become a Production-Ready AI Engineer with the AI Engineer 360 Course.

CONNECT & LEARN WITH US
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https://www.thetechzeen.com/

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Get Lecture Notes (GitHub):
https://github.com/farzeen-ali/AI-Engineer-360

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