RAG Explained: Why Your LLM Has Amnesia скачать в хорошем качестве

RAG Explained: Why Your LLM Has Amnesia

Large Language Models are powerful — but they have a fundamental limitation: they don’t remember anything outside their training data. In this video, I explain Retrieval-Augmented Generation (RAG) — the system design pattern that gives LLMs access to external knowledge at runtime and turns them into production-ready AI systems. Drawing from real-world experience building ML and LLM systems at scale, this talk covers: Why LLMs hallucinate and why prompts alone don’t fix it What RAG is (and what it is not) Chunking strategies and why they matter Embeddings, vector databases, and ANN search How retrieval context is injected into LLM prompts The economics of RAG and why systems like Perplexity work Why RAG is a system, not a single model or prompt This video is aimed at ML engineers, data scientists, and practitioners looking to move from LLM demos to reliable, scalable production systems. Image courtesy: https://www.dailydoseofds.com/ #RAG #LLM #GenerativeAI #VectorDatabases #Embeddings #MachineLearning #AIEngineering #MLOps