Self-RAG Tutorial: How to Make Your AI Fact-Check Itself | Advanced RAG | CampusX

CampusX · Beginner ·🛠️ AI Tools & Apps ·2mo ago

Skills: Advanced RAG90%RAG Basics60%

In this video, we dive deep into Self-RAG (Self-Reflective Retrieval-Augmented Generation), a powerful technique designed to fix the biggest flaws in traditional RAG systems: unnecessary retrievals, irrelevant documents, and hallucinations Resources: https://github.com/campusx-official/self-rag OG RAG Tutorial: https://youtu.be/X0btK9X0Xnk Courses: https://learnwith.campusx.in/s/store Queries: https://www.instagram.com/campusx.official 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at support@campusx.in ⌚Chapters⌚ 00:00 - Introduction to Advanced RAG Techniques 01:00 - Prerequisites & Disclaimer 01:48 - 3 Major Problems with Traditional RAG 02:14 - Problem 1: Indiscriminate/Unnecessary Retrieval 05:17 - Problem 2: Blind Trust in Documents 06:55 - Problem 3: Lack of Answer Verification 07:34 - What is Self-RAG? (Self-Reflective RAG) 08:24 - The 4 Key Questions Self-RAG Answers 12:40 - Architectural Overview & Logic 15:05 - Step 1: Implementing the Retrieval Decision Node 35:30 - Step 2: Filtering Relevant Documents 41:00 - Step 3: Generating Answers from Context 45:56 - Step 4: Detecting Hallucinations (Support Node) 53:36 - Step 5: Implementing the Revised Answer Loop 58:23 - Step 6: Testing Answer Usefulness & Query Rewriting 01:08:13 - Conclusion & Final Summary

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Chapters (16)

Introduction to Advanced RAG Techniques

1:00 Prerequisites & Disclaimer

1:48 3 Major Problems with Traditional RAG

2:14 Problem 1: Indiscriminate/Unnecessary Retrieval

5:17 Problem 2: Blind Trust in Documents

6:55 Problem 3: Lack of Answer Verification

7:34 What is Self-RAG? (Self-Reflective RAG)

8:24 The 4 Key Questions Self-RAG Answers

12:40 Architectural Overview & Logic

15:05 Step 1: Implementing the Retrieval Decision Node

35:30 Step 2: Filtering Relevant Documents

41:00 Step 3: Generating Answers from Context

45:56 Step 4: Detecting Hallucinations (Support Node)

53:36 Step 5: Implementing the Revised Answer Loop

58:23 Step 6: Testing Answer Usefulness & Query Rewriting

1:08:13 Conclusion & Final Summary

This is the most useful n8n workflow I have ever built...