RAG Programming 2026 - Part 1/4 - Chunks, Embedding and Vector Databases

AI engineering for beginners How does RAG actually work behind the scenes? 🤔 In Part 2 of this RAG (Retrieval-Augmented Generation) series, we’ll understand the two most important stages of a RAG pipeline: Ingestion and Retrieval, explained step by step using a simple whiteboard explanation. 📚 What you'll learn in this video: ✅ What is Data Ingestion in RAG? ✅ How documents are processed and divided into chunks ✅ What are Embeddings, and how do they work? ✅ What is a Vector Database, and why do we need it? ✅ How does Retrieval work in RAG? ✅ What are Query Embeddings and Similarity Search? ✅ How are the most relevant chunks retrieved? ✅ How does the retrieved context help an LLM generate better answers? 🎯 Who should watch this? This video is for beginners who want to understand RAG, LLMs, Generative AI, and AI application development from scratch, without getting lost in complicated technical jargon. No complicated code — just clear concepts explained on a whiteboard! 📺 Watch the complete RAG series: Subscribe to the channel and follow the upcoming videos to learn RAG step by step in Hindi. 👍 If this explanation helped you, like the video, share it with someone learning AI, and subscribe for more easy-to-understand AI tutorials in Hindi. #RAG #GenerativeAI #AIinHindi