Learn how to enhance your Retrieval Augmented Generation (RAG) pipeline through Contextual Retrieval in this 18-minute technical video from a Machine Learning researcher. Explore the fundamentals of RAG and dive into advanced concepts including semantic embeddings, traditional retrieval methods like TF-IDF and BM25, and the innovative Contextual Retrieval technique recently introduced by Anthropic. Master the implementation details, performance considerations, and optimization strategies for combining these approaches to create more effective RAG systems. Through detailed explanations and practical illustrations, gain insights into calculating BM25 scores, integrating various retrieval methods, and boosting overall system performance with the Claude family of models.
Improving RAG Pipelines with Contextual Information Retrieval
Overview
Syllabus
- Intro
- What is RAG?
- Semantic similarity and Lexical Matching
- TF-IDF and BM25
- BM25 calculation illustration
- BM25 & TF-IDF in RAG
- Contextual Retrieval
- Implementing Contextual Retrieval
- Performance with Contextual Retrieval
- Implementation Considerations
- Boosting performance
- Conclusion
Taught by
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