How RAG Actually Works: The Full Pipeline, Chunking Techniques & Hallucination Fixes

Ever wondered how tools like ChatGPT or Notion AI can answer questions about YOUR documents — PDFs, manuals, internal wikis — even though they were never trained on them? That's RAG: Retrieval Augmented Generation. In this video, we build up the entire pipeline, piece by piece, with diagrams at every step. This is a direct follow-up to our LLM Foundations video — if you haven't seen how tokens, embeddings, and transformers work yet, start there first. 🔹 What you'll learn: What RAG actually is — the open-book exam analogy that makes it click The two phases every RAG system has: Indexing and Answering What a vector database is, and why it searches by similarity instead of keywords Chunking — why it's the single most important design decision in RAG All 7 chunking techniques: fixed-size, sentence-based, paragraph-based, semantic, parent-child, structure-aware, and recursive The full RAG pipeline, end to end — from raw documents to a grounded answer Reranking — why retrieval runs fast-and-rough first, then slow-and-precise The 5 stages where RAG can actually fail 5 concrete strategies to fix hallucinations in a RAG system 📋 Full glossary of every term covered: pinned comment below. If this is part of a series you're following, subscribe to LogicAndLoops — more LLM and AI engineering breakdowns coming soon. #RAG #RetrievalAugmentedGeneration #LLM #AI #MachineLearning #VectorDatabase #NLP