Master Chunking,Embeddings & VectorDB for RAG:Complete Guide

Description

Master the essential building blocks of modern Retrieval-Augmented Generation (RAG) systems in this in-depth tutorial! We cover the full pipeline step by step: • Chunking strategies — Why proper chunking is critical, fixed-size vs semantic vs recursive chunking, smart overlap techniques, context preservation, and common mistakes • Text Embeddings — How embeddings convert text into high-dimensional vectors, popular models (OpenAI, sentence-transformers from Hugging Face, Cohere, etc.), dimension trade-offs, and choosing the right embedding model • Vector Databases — What vector databases actually do, comparison of popular options (ChromaDB, Pinecone, Weaviate, Qdrant, Milvus, pgvector), how approximate nearest neighbor search works (HNSW, IVF, etc.), cosine/Euclidean/dot-product similarity, and efficient storage + retrieval Ideal for developers, data scientists, and AI builders working on semantic search, intelligent chatbots, document Q&A systems, or production RAG applications — whether you’re using LangChain, LlamaIndex, Haystack, or building from scratch. If you’re serious about building powerful LLM-powered applications, understanding these three pillars (chunking + embeddings + vector DB) is non-negotiable. 👍 Like the video if it helped! 🔔 Subscribe for more practical GenAI / RAG / LLM engineering content 💬 Drop a comment: Which vector database are you using / planning to use? #RAG #VectorDatabase #Embeddings #SemanticSearch link:- https://youtu.be/dSsa06994mQ?si=upJpep59sPt2wnnm