Why Your RAG Pipeline Lies to You

1. 1Evaluate your RAG pipeline's performance on a held-out test set to identify potential biases
2. 2Analyze the data distribution and ensure it is representative of real-world scenarios
3. 3Test your pipeline with adversarial examples to reveal potential weaknesses
4. 4Compare the performance of your pipeline with other models or baselines
5. 5Investigate the embedding space and vector database to ensure proper configuration

Data scientists and engineers working with RAG systems can benefit from understanding the limitations and potential biases of their pipelines

💡 RAG pipelines can be biased or inaccurate if not properly evaluated and configured

🚨 Your RAG pipeline may be lying to you! 🚨 Learn how to identify and address potential issues