Day 10 RAG, AI Agents & Recommendation Systems in Deep Learning Explained, LangGraph, Vector Search

Ek chaunkaane waali sacchai: ChatGPT ka RAG, Cursor ka codebase samajhna, aur Netflix/YouTube ke recommendations — teeno, andar se, EK hi idea hain. Cheezon ko ek “meaning-space” mein points banao, aur sabse paas waale dhoondo. Bas. Aaj hum modern AI ke asli, production systems ke dil tak jaate hain — pure Hindi mein, poori tarah diagram-first (3Blue1Brown style). Is video mein aap seekhoge: • Embeddings — meaning kaise geometry banti hai — similarity, nearest-neighbour, ANN, vector DBs, HNSW • RAG — indexing, retrieval, augment/generate, chunking, reranking, hybrid search, evaluation, RAG vs fine-tuning • AI Agents — tools/function-calling, think→act→observe loop, MCP, LangGraph — aur Cursor bade codebases kaise samajhta hai • Recommenders — user-item matrix, collaborative filtering, matrix factorization, two-tower, graph & sequential — plus ek hands-on CoreRec Aur end mein — wo dhaaga jo in sabko jodta hai. TIMESTAMPS: ● Chapter 1 — Generative AI & the building blocks 0:00 Intro — the systems we'll build 0:51 What generative models do 2:05 Embeddings — meaning as geometry 3:22 Cosine similarity 4:36 Nearest-neighbour search 5:43 The scale problem (ANN) 6:51 Vector databases 8:11 HNSW 9:33 Chapter 1 recap ● Chapter 2 — RAG (Retrieval-Augmented Generation) 10:40 RAG — why 11:50 The RAG pipeline 13:02 Indexing 14:16 Retrieval 15:26 Augment & generate 16:47 Chunking 18:05 Reranking 19:26 Hybrid search 20:47 Evaluating RAG 22:18 RAG vs fine-tuning 23:49 Chapter 2 recap ● Chapter 3 — Orchestration (LangChain · Agents · LangGraph · Cursor) 25:23 Orchestration — intro 26:34 LangChain 27:54 Tools / function calling 29:26 Agents (the loop) 30:57 MCP 32:37 Why LangGraph 34:03 LangGraph — a state-graph 35:37 Cursor — indexing a codebase 37:00 Cursor — symbol graph 38:38 Chapter 3 recap ● Chapter 4 — Recommendation systems + CoreRec 40:16 Recommendation systems 41:28 The user-item matrix 42:43 Collaborative filtering 44:08 Matrix factorization 45:44 Two-tower models 47:13 Content-based & hybrid 48:42 Graph & sequential 50:18 CoreRec intro 51:42 CoreRec hands-on 53:42 Recap + the one idea TAGS rag explained hindi, retrieval augmented generation hindi, ai agents hindi, langchain hindi, langgraph, mcp model context protocol, cursor ai kaise kaam karta hai, vector database, embeddings explained hindi, hnsw, nearest neighbour search, recommendation system hindi, matrix factorization, collaborative filtering, two tower model, corerec, llm hindi, deep learning hindi, generative ai hindi, 3blue1brown hindi, machine learning hindi, mathio, day 10,Vishesh yadav