AI Mechanics

AI Embeddings Explained: Vectors Behind RAG Search

Name: AI Embeddings Explained: Vectors Behind RAG Search
Uploaded: 2026-08-21T14:53:32+00:00
Description: What are embeddings? They turn text into vectors so semantic search can find nearest neighbors by meaning, not spelling. This visual explanation shows cosine similarity, chunking, HNSW, and why RAG retrieval depends on that vector space. Watch next: RAG - https://www. youtube. com/watch? v=qxN9X9nMPmE Vector Databases is the next episode in this chain and is in production.

Dr. Ibrar AhmedAugust 21, 20263 min read

What are embeddings? They turn text into vectors so semantic search can find nearest neighbors by meaning, not spelling. This visual explanation shows cosine similarity, chunking, HNSW, and why RAG retrieval depends on that vector space. Watch next: RAG - https://www. youtube. com/watch? v=qxN9X9nMPmE Vector Databases is the next episode in this chain and is in production.

What you will learn • One-hot vs dense vectors and cosine similarity • Word2Vec, skip-gram, static vs contextual embeddings • Sentence embeddings and semantic search • How embeddings power RAG pipelines • HNSW, dimensions, storage cost, query vs document embeddings Playlists Embeddings, Vector Search & RAG: https://www.

youtube. com/playlist? list=PLbCgIqEVrFcE LLM Fundamentals: https://www. youtube. com/playlist? list=PLYnF4PwCs3xg DEEP AI archive: https://www. youtube. com/playlist? list=PLK3S1GR94Fzg

Watch the video for the full walkthrough. Use this page when you want the argument in writing without scrubbing the timeline.