Your RAG is getting worse and nothing in your code changed
📰 Dev.to · Mukunda Rao Katta
Learn how to identify and address issues with RAG system accuracy that aren't related to code or data changes
Action Steps
- Investigate data drift using tools like statistical process control to detect changes in data distribution
- Analyze model performance over time to identify trends and patterns in accuracy decline
- Check for concept drift by monitoring changes in the relationship between input data and target variables
- Test for changes in user behavior or query patterns that may affect RAG system performance
- Apply techniques like data augmentation or transfer learning to adapt the RAG system to changing conditions
Who Needs to Know This
Data scientists and machine learning engineers can benefit from understanding the potential causes of declining RAG system accuracy, and how to troubleshoot and resolve these issues
Key Insight
💡 RAG system accuracy can decline over time due to factors like data drift, concept drift, and changes in user behavior, even if the code and data remain unchanged
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🚨 Your RAG system's accuracy is declining, but your code hasn't changed! 🤔 Learn how to identify and fix the issue
Key Takeaways
Learn how to identify and address issues with RAG system accuracy that aren't related to code or data changes
Full Article
I had a RAG system that was 92% accurate on Monday and 78% accurate three weeks later with no code or data changes. Here is what was actually moving.
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