Learn about the latest advancements in Retrieval-Augmented Generation (RAG) through a detailed 34-minute technical video that explores Anthropic's innovative contextual retrieval system. Dive deep into solutions for common RAG challenges, including the implementation of BM25 for exact term matching and an analysis of vector space limitations. Master practical techniques like prompt caching, contextual preprocessing, and reranking methodologies that can be applied across various LLM platforms including Google and Mistral. Follow along with comprehensive code demonstrations, performance benchmarks, and implementation recommendations while accessing official cookbook examples from Anthropic's GitHub repository. Gain hands-on experience with step-by-step guidance for setting up contextual retrieval systems, complete with preprocessing workflows and detailed performance metrics to optimize your RAG applications.
Anthropic's Improved Contextual RAG System - Implementation Guide and Performance Analysis
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Overview
Syllabus
The Problem with RAG
Add BM25 for exact term match
My explanation of the Vector Space failure
Anthropic new Contextual Retrieval new idea
Generating prompt for Contextual Retrieval
Detailed code for Contextual Retrieval
Contextual Retrieval Preprocessing
Prompt caching explained
Absolute improvements
ReRanking for Contextual prompts
Recommendations for NEW ContextualRAG
Performance benchmarks ContextualRAG
Anthropic GitHub cookbook code
Taught by
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