RAG Explained From Scratch: The Full Pipeline, Step by Step

What is RAG, and how does a RAG system actually work? In this first video on the channel, I walk through the full Retrieval-Augmented Generation pipeline from scratch, using diagrams: how documents get chunked, turned into embeddings, stored in a vector database, and then retrieved, reranked and passed to an LLM to answer a question with sources. Whether you're just getting into AI or you're already an engineer, this should give you a clear mental model of RAG before we get into the production side in upcoming videos. What's covered: 00:00 Intro 01:05 What problem does RAG solve? 03:42 Chunking (and why overlap matters) 05:20 Embeddings 07:51 Vector databases & metadata 10:01 Query embedding & vector search (top-k, cosine similarity) 13:08 Reranking 14:29 Prompt assembly & the LLM 16:08 The complete RAG pipeline, end to end 18:12 What a production RAG system adds If this helped, subscribe for the next videos on production RAG: evaluation, security, monitoring and deployment. Let me know in the comments which part you want me to go deeper on. #RAG #RetrievalAugmentedGeneration #LLM #AIEngineering #GenerativeAI #VectorDatabase