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technologyreview.com·1h ago
Data Science
Towards Data Science·Jul 3·5 min read
The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation
TL;DR
Mainstream retrieval methods often rely on cosine similarity for ranking, which may not be the most effective approach. The article argues that retrieval should be viewed as filtering structured data rather than searching unstructured text, with embeddings as a secondary option.
✦ Why It Matters
Engineers can enhance retrieval systems by adopting a filtering approach rather than relying solely on cosine similarity.
Key Takeaways
- Consider retrieval as filtering structured data, not just searching text.
- Use embeddings as a fallback, not the primary method.
- Differentiate between anchor and context for better retrieval granularity.
- Explore the provided GitHub notebooks for practical implementation.
Full Summary
Retrieval-Augmented Generation (RAG) systems typically prioritize cosine similarity for ranking results, but this article challenges that norm. It posits that retrieval should be understood as filtering structured tables instead of searching through free text, suggesting that embeddings should serve as an optional fallback rather than the primary method.
The authors introduce two granularities—anchor and context—to refine the retrieval process. By emphasizing these distinctions, they provide a framework that can be quantitatively assessed.
The implications of this approach could lead to more efficient data retrieval systems, as it encourages engineers to rethink their methodologies. The article also includes runnable notebooks on GitHub for practical application of these concepts.