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Explore How to Build RAG Hybrid Search | Step By Step with 19,072 views, 506 likes, and 101 comments. Experience the impact of this video content that has captured audience attention.

📌 View all Projects: https://www.youtube.com/playlist?list=PLN6lWsI6DyWSb-LB-mZFCdsIVPuf9EMxA 📌 Project 1: https://youtu.be/Dqg0eAHDmhE 📌 RAG Explained in 5 Mins: https://youtu.be/T4oZZo9bIEo Most RAG tutorials show you how to embed a PDF and get answers. That's not production RAG. ⏱ Timestamps: 00:00 - Why Production RAG is different 01:40 - Members Update 02:19 - Document Ingestion 03:01 - RAG Fails - Demo 05:00 - Why Character Chunking Broke? 05:46 - Structure Based Chunking 06:51 - RAG Application Flow 07:30 - Citations - Metadata Design 11:05 - Code Walkthrough 16:07 - RAG Final Demo06 18:55 Outro In this video, I'll show you exactly why a naive RAG system breaks on technical documentation — and how to fix it by building a Documentation Assistant the right way. 🔧 What you'll learn: → Why character chunking fails on technical docs → Structure-based semantic chunking → Hybrid Search: BM25 + Semantic Search combined → Reciprocal Rank Fusion (RRF) for better retrieval → Citation design so engineers trust the answers → Strict non-hallucination — the system says "I don't know" when it doesn't know This is Project 2 of the AI Engineering Roadmap series — each project builds real production-grade AI systems, not tutorial demos. 🔔 Subscribe for more production AI engineering projects.

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