Chapters (33)
- 0:00Intro
- 1:44Full RAG Overview
- 8:27Development Environment Setup
- 15:35Document Loader - Overview
- 28:27Document Processing Pipeline - RAG Indexing Pipeline
- 48:12Embedding Dimensions - Deep Dive
- 1:01:05Hands-on - Create a Vector DB Using Chroma
- 1:17:48Similarity Search with Scores
- 1:24:32Building a Basic RAG System
- 1:33:16Debugging RAG Systems
- 1:53:46Hybrid Search
- 1:13:49Token Budgeting
- 2:21:10Observability - Introduction
- 2:29:56LangSmith Setup
- 2:37:56RAG Optimization
- 3:12:58Scaling RAG Systems
- 3:23:35The Real Costs of Vector Search
- 3:33:17Production Hosting
- 3:36:00Supabase and PGVector - Set up and Introduction
- 4:04:41Three Pillars of Production Visibility
- 4:16:11Production Project
- 4:34:36Set up the Security Layer
- 5:27:46Test the Security Layer
- 5:41:36Security Checklist
- 6:06:09Advanced RAG Topics - Long Context Models vs RAG
- 6:14:29Contextual Retrieval
- 6:24:26Late Chunking vs Early Chunking
- 6:42:04Agentic RAG - Self-Correcting Retrieval
- 7:04:45GraphRAG - Multi-hop Reasoning
- 7:24:28Multimodal RAG - ColPali - Vision-Based Document RAG
- 7:34:45Summary - Advanced RAG (Current State)
- 7:37:02RAG Evolution - Overview
- 7:38:35Outro
Show the creator's full description
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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👾 @goddardtan
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👾 @kikilogsin
👾 @anthonycampbell2148
👾 @tobymiller7790
👾 @rajibdassharma497
👾 @CloudVirtualizationEnthusiast
👾 @adilsoncarlosvianacarlos
👾 @martinmacchia1564
👾 @ulisesmoralez4160
👾 @_Oscar_
👾 @jedi-or-sith2728
👾 @justinhual1290
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