Generative AI Episode 10: Retrieval Augmented Generation — Make AI Answer From Your Own Documents

Retrieval augmented generation is a long name for a simple idea: look things up first, then answer. In this episode we build a small ask-my-document assistant that reads one short document and answers questions about it - with no training and no fine-tuning. We start with why a language model alone cannot help with your own files, then walk the whole pattern: retrieve the relevant chunks, augment the prompt with them, and generate an answer grounded in what was retrieved. You will see the exact prompt template, a supported question answered with a citation, and an unsupported question answered honestly instead of invented. What you will learn: Why a model's frozen training knowledge fails on your private documents What retrieval, augmented and generation actually mean The full RAG pipeline from document chunks to a generated answer The prompt template that grounds the answer in retrieved excerpts Why citations and honest refusals make an assistant trustworthy Project 3: build an ask-my-document assistant from one short document CHAPTERS 00:00 A question answered from your own document 00:37 Why the model alone is not enough 01:12 Retrieval, augmented, generation 01:46 The RAG pipeline 02:22 What augmentation actually sends 02:52 Grounding and honest refusals 03:21 Project 3: the ask-my-document assistant 03:52 Test 1: a supported question 04:24 Test 2: an unsupported question 04:56 Recap and what is next This is Episode 10 of the Generative AI series - from AI beginner to AI architect. Episode 9: Vector Databases Explained -    • Generative AI Episode 9: Vector Databases ...   Episode 8: What Are Embeddings -    • Generative AI Episode 8: What Are Embeddin...   Episode 7: Prompt Engineering for Beginners -    • Generative AI Episode 7: How to Write Bett...   #RetrievalAugmentedGeneration #RAG #GenerativeAI