RAG Explained: Give AI Your Own Knowledge

RAG Explained: Give AI Your Own Knowledge Your AI agent is smart. But ask it something only your company knows, how do we restart the payments service, what's in our runbook and it will confidently make something up. Not because it's dumb, but because it was trained on the public internet, never on your docs. This episode is about the fix, and it has a name: RAG, Retrieval-Augmented Generation. And no, RAG does NOT mean training the model on your data. This is Part 1 of a 3-part RAG arc: the concepts, in plain English, with tiny live demos embeddings, similarity, and retrieval so you actually understand how RAG works before we build the real thing. ▶️ CATCH UP ON THE SERIES Episode 2 Build an AI Agent from Scratch:    • Build an AI Agent from scratch   Episode 3 Build Your First MCP Server:    • Build Your First MCP Server   Episode 4 Give Your AI Agent Superpowers with MCP:    • Give Your AI Agent Superpowers with MCP   🧑‍💻 Code (all four demos + README): 👉 https://github.com/kalpesh6331/Explor... ⏱️ Chapters 00:00 Introduction 00:22 DEMO 01 - AI does not know it 02:51 Solution to the problem 04:36 Embedding explained 06:06 DEMO 02 - How Embedding works 07:23 Cosine Similarity 08:25 DEMO 03 - Calculation of Similarity 10:48 RAG Architecture 12:12 DEMO 04 - Retrieval in action 14:08 Summary 📚 What you'll learn • What RAG actually is and the #1 misconception (no, you are NOT training the model) • Fine-tune vs. stuffing everything into the prompt vs. RAG and why RAG wins for "know my docs" • Embeddings: turning text into numbers that capture meaning, running locally on your machine • Similarity: how a computer measures that two sentences mean the same thing (cosine similarity) • Chunks and vector databases. • Retrieval in ~10 lines of Python the whole idea, no database, no magic • The two-phase RAG architecture: index your docs once, answer questions every time #RAG #RetrievalAugmentedGeneration #AI #LLM #Embeddings #VectorDatabase #Qdrant #DevOps #Python #Claude #Anthropic #AIEngineering #SRE #PlatformEngineering