Your RAG Eval Set Is Probably Wrong. The Test That Catches It.
📰 Dev.to · Gabriel Anhaia
Learn how to identify and fix common issues in RAG evaluation sets, including leakage, drift, and judge bias, and get a simple drift detector to use in production
Action Steps
- Identify potential leakage in your eval set by checking for data overlaps between training and testing sets
- Detect drift in your eval set using a drift detector, such as the 40-line code provided
- Evaluate your eval set for judge bias by analyzing the distribution of labels and annotations
- Apply techniques to mitigate leakage, drift, and judge bias, such as data augmentation and active learning
- Test and validate your RAG model using the corrected eval set
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this knowledge to improve the accuracy and reliability of their RAG models, while product managers can use this insight to inform their product development and testing strategies
Key Insight
💡 Common issues in RAG evaluation sets, such as leakage, drift, and judge bias, can significantly impact model performance and reliability
Share This
🚨 Your RAG eval set might be wrong! 🚨 Learn to detect leakage, drift, and judge bias, and get a simple drift detector to fix it #RAG #MachineLearning
Key Takeaways
Learn how to identify and fix common issues in RAG evaluation sets, including leakage, drift, and judge bias, and get a simple drift detector to use in production
Full Article
Three ways eval sets go wrong in production: leakage, drift, judge bias. Plus a 40-line drift detector you can ship today.
DeepCamp AI