RAG from First Principles — How to Turn an LLM Into a Reasoning Engine

Every LLM is a confident liar. Studies show hallucination rates of 15-27% on knowledge-intensive tasks. Fine-tuning can't fix it — but RAG can.
Retrieval-Augmented Generation shifts your LLM from "knowledge recall" to "reading comprehension." This video covers the full stack: chunking, embeddings, vector search, re-ranking, and evaluation — no fluff, just the engineering that makes it work.
📑 CHAPTERS:
0:00 — The Problem RAG Solves (Cutoffs, Hallucination, Private Data)
0:45 — Core Pipeline: Indexing and Querying
1:30 — Chunking: The Most Overlooked Hyperparameter
3:00 — Embeddings and Vector Search
3:45 — Retrieval Quality: Re-ranking and Hybrid Search
4:45 — Advanced Patterns: Self-RAG, Multi-Hop, Agentic RAG, GraphRAG
5:30 — Evaluation: How to Know If Your RAG Works
6:15 — Limitations: What RAG Still Gets Wrong
6:45 — Where RAG Is Going (Multimodal, Edge, Invisible RAG)
7:15 — Call to Action
Go build a RAG system this week. pip install chromadb — that's all it takes to start.

Видео RAG from First Principles — How to Turn an LLM Into a Reasoning Engine канала Jeff Heidelberger