Why Traditional Databases are Failing AI (Vector DB Guide)

Description

Vector Databases are the hidden engine behind LLMs, RAG, and semantic search. In this Zero to Hero guide, we move beyond the hype to understand the deep tech: how vectors, embeddings, and complex indexing (IVF, HNSW) actually work in 2026. Chapters Timestamp: 0:00 – 0:03 – Intro to vector databases 0:03 – 0:10 – AI powering modern databases 0:10 – 0:20 – Evolution: relational → unstructured → vector 0:20 – 0:28 – Vector DBs focus on semantic similarity 0:28 – 0:44 – Bridging human-like understanding 0:44 – 1:03 – Relational vs vector: spreadsheets vs multiverse 1:03 – 1:18 – Embeddings: numerical representation of features 1:18 – 1:36 – Visualizing vector space & clustering 1:36 – 1:54 – Embedding models for text, images & audio 1:54 – 2:10 – Retrieval-augmented generation (RAG) use case 2:12 – 2:40 – Phase 1: ingestion, cleaning & chunking 2:40 – 3:00 – Chunking strategies & preserving context 3:00 – 3:18 – Similarity metrics: Euclidean & cosine 3:18 – 3:36 – Recommendation systems & cosine advantage 3:36 – 4:02 – Scaling challenges & ANN indexing 4:02 – 4:28 – HNSW & IVF indexing for speed & memory 4:28 – 4:57 – Sharding & replication for massive data 5:00 – 5:16 – CRUD support & language bindings 5:16 – 5:39 – Use cases: recommendations & media search 5:39 – 6:07 – Anomaly detection & fraud spotting 6:07 – 6:18 – Native vs vector-enabled databases 6:21 – 6:41 – Semantic drift & model retraining 6:41 – 7:00 – Computational costs & cloud scaling 7:00 – 7:12 – Summary: meaning, memory, & fast search 7:13 – 7:21 – Outro & subscribe call-to-action Don't forget to Like and Subscribe for more AI Engineering deep dives! #VectorDatabase #embeddings #RAG #MachineLearning #vectorsearch #LLM