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[[{“value”:”Learn to build, debug, optimize, and scale RAG systems for production.
🚀 Free Production AI Starter Kit: https://bit.ly/production-ai-pack
This course teaches what tutorials skip: why 90% of RAG projects fail and how to fix them.
💻 Code, Parts 1-5: https://github.com/pdichone/production-course-main-code
💻 Code, Part 6: https://github.com/pdichone/fcc-production-rag-part-6
Paulo’s channel: @vincibits
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that’s reinvented interactive learning: https://scrimba.com/freecodecamp
⭐️ Chapters ⭐️
0:00:00 Intro
0:01:44 Full RAG Overview
0:08:27 Development Environment Setup
0:15:35 Document Loader – Overview
0:28:27 Document Processing Pipeline – RAG Indexing Pipeline
0:48:12 Embedding Dimensions – Deep Dive
1:01:05 Hands-on – Create a Vector DB Using Chroma
1:17:48 Similarity Search with Scores
1:24:32 Building a Basic RAG System
1:33:16 Debugging RAG Systems
1:53:46 Hybrid Search
1:13:49 Token Budgeting
2:21:10 Observability – Introduction
2:29:56 LangSmith Setup
2:37:56 RAG Optimization
3:12:58 Scaling RAG Systems
3:23:35 The Real Costs of Vector Search
3:33:17 Production Hosting
3:36:00 Supabase and PGVector – Set up and Introduction
4:04:41 Three Pillars of Production Visibility
4:16:11 Production Project
4:34:36 Set up the Security Layer
4:16:11 Set up the LangGraph Agent and the FastAPI API – Testing and LangSmith Observability Dashboard
5:27:46 Test the Security Layer
5:41:36 Security Checklist
6:06:09 Advanced RAG Topics – Long Context Models vs RAG
6:14:29 Contextual Retrieval
6:24:26 Late Chunking vs Early Chunking
6:42:04 Agentic RAG – Self-Correcting Retrieval
7:04:45 GraphRAG – Multi-hop Reasoning
7:24:28 Multimodal RAG – ColPali – Vision-Based Document RAG
7:34:45 Summary – Advanced RAG (Current State)
7:37:02 RAG Evolution – Overview
7:38:35 Outro
🎉 Thanks to our Champion and Sponsor supporters:
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—
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