Summary

The article introduces a new method called Contextual Retrieval, which enhances the traditional Retrieval-Augmented Generation (RAG) approach by improving the retrieval step through two sub-techniques: Contextual Embeddings and Contextual BM25. This method significantly reduces retrieval failures and improves the accuracy of AI models in specific contexts. The article also discusses the importance of context in information retrieval and provides insights into implementing this technique using Claude, along with considerations for performance optimization and cost reduction.

💭 Your Thoughts

Run a prompt for each chunks to generate the context still could be costy.

Data Problems Addressed