How to Evaluate Your RAG Pipeline

📰 Dev.to · Dave Graham

Learn to evaluate your RAG pipeline by checking both retrieval and generation failures to improve overall performance

intermediate Published 24 May 2026
Action Steps
  1. Identify potential failure points in your RAG pipeline
  2. Implement metrics to measure retrieval accuracy
  3. Evaluate generation quality using metrics such as perplexity and coherence
  4. Compare retrieval and generation performance to identify bottlenecks
  5. Apply fixes to address identified issues and improve overall pipeline performance
Who Needs to Know This

Data scientists and engineers working with RAG pipelines can benefit from this evaluation process to identify and fix potential issues, leading to improved model performance and reliability

Key Insight

💡 Evaluating both retrieval and generation failures is crucial to improving the overall performance of a RAG pipeline

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🚀 Improve your RAG pipeline by evaluating both retrieval and generation failures #RAG #AI

Key Takeaways

Learn to evaluate your RAG pipeline by checking both retrieval and generation failures to improve overall performance

Full Article

RAG has two places to fail: retrieval and generation. Most teams only catch one. Here's the complete...
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