Reranking Explained: The Second Opinion That Fixes RAG Search

Why does your RAG chatbot sometimes answer from the wrong document? Vector search is fast but rough: it compares embeddings made separately, so close-but-wrong chunks sneak into the top results. This video explains reranking from zero: bi-encoders vs cross-encoders, the retrieve-wide-then-rerank pattern, what a reranker costs in latency and money, and using an LLM as the judge. Part 6 of a series that takes you from zero to building RAG yourself. This is part 6 of the RAG series on Zero 2 Concept: ideas, explained from zero. CHAPTERS 0:00 The Wrong Top Result 0:44 Why Vector Search Is Rough 1:50 A Second Opinion 2:40 Two Kinds of Model 3:51 Why Not Rerank Everything? 4:28 Retrieve Wide, Rerank Narrow 5:53 What It Costs 6:56 An LLM as the Judge 7:33 Try It Yourself 8:24 Checkpoint 8:52 What's Next Previous: Part 5 — RAG Prompt Engineering: How to Ground an LLM in Retrieved Chunks    • RAG Prompt Engineering: How to Ground an L...   Next: Part 7 — Hybrid Search Explained: BM25 + Embeddings Merged with RRF    • Видео   Start from Part 1:    • AI Hallucinations Explained: Why AI Lies (...   Full series: RAG from Zero: Retrieval-Augmented Generation Explained    • RAG from Zero: Retrieval-Augmented Generat...