Stop doing unscoped embeddings: 7 reasons relevance decays

1. 1Recognize the limitations of context-free embeddings
2. 2Assess your current embedding pipeline for potential relevance decay
3. 3Implement scoped or context-aware embeddings to improve retrieval quality
4. 4Evaluate the impact of scoped embeddings on your system's performance
5. 5Refine your embedding pipeline based on the evaluation results
6. 6Explore alternative embedding techniques, such as RAG, to further optimize retrieval quality

Data scientists and machine learning engineers working on information retrieval systems can benefit from understanding the limitations of broad embeddings and how to optimize them for better performance

💡 Unscoped embeddings can lead to relevance decay, but using context-aware approaches can significantly improve retrieval quality

💡 Did you know broad embeddings can poison retrieval quality? Learn why and how to fix it with context-aware approaches