Advanced RAG: How Corrective RAG (CRAG) Solves Traditional RAG Problems | CampusX

CampusX · Intermediate ·📄 Research Papers Explained ·1mo ago
Traditional RAG systems often suffer from "blind trust," where they generate answers based on irrelevant retrieved documents, leading to hallucinations. In this video, we explore Corrective RAG (CRAG), a robust architecture that evaluates the quality of retrieval before generating a response. We walk through the first principles of CRAG, moving from a traditional RAG setup to a complete system featuring Retrieval Evaluation, Knowledge Refinement, and Web Search integration using tools like Tavily. Whether the retrieval is correct, ambiguous, or incorrect, you'll learn how to ensure your LLM a…
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Chapters (11)

What is Corrective RAG (CRAG)?
1:12 The problem with Traditional RAG: "Blind Trust" & Hallucinations
2:00 Visualising the Vector Database & Retrieval Workflow
4:22 Practical Example: When LLMs fail on "Out of Distribution" queries
7:04 Code Walkthrough: Loading ML books and creating a basic Retriever
10:17 Testing the Baseline: Bias-Variance Tradeoff vs. Recent AI News
13:51 Identifying Hallucinations in the Transformer architecture query
15:53 Deep Dive: The CRAG Research Paper & Proposed Architecture
17:20 The 3 Retrieval Cases: Correct, Incorrect, and Ambiguous
21:01 Retrieval Evaluator: Refining Internal vs. External Knowledge
23:02 Iteration 1: Knowledge
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