Module 10: RAG Evaluation & Quality Engineering Slides

Slide Outline

1. RAG Evaluation & Quality Engineering - Hallucination detection, retrieval metrics, groundedness scoring, and evaluation frameworks
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - Without evaluation, you do not know if your RAG system is improving or degrading. Every change — new model, new chunking
4. Retrieval Metrics - Lesson section from the full module
5. Groundedness Scoring - Lesson section from the full module
6. Hallucination Detection - Lesson section from the full module
7. Continuous Evaluation - Lesson section from the full module
8. Common Mistakes to Avoid - 4 mistakes covered
9. Hands-On Labs - 2 hands-on labs
10. Key Takeaways - 5 points to remember

Learning Objectives

• Measure retrieval quality with precision and recall
• Detect and score hallucinations in generated answers
• Build automated evaluation pipelines
• Design continuous quality monitoring for production RAG

Why This Module Matters

Without evaluation, you do not know if your RAG system is improving or degrading. Every change — new model, new chunking, new retrieval — needs measurement. This module gives you the framework to quantify and continuously monitor RAG quality.

Common Mistakes

• Only evaluating end-to-end (cannot tell if retrieval or generation is the problem)
• No test dataset (no way to measure improvement objectively)
• Not automating evaluation (manual review does not scale)
• Ignoring hallucination detection (users lose trust after one wrong answer)

Key Takeaways

• Evaluate retrieval and generation SEPARATELY — bad retrieval causes bad answers
• Precision@K and Recall@K are the most important retrieval metrics
• Groundedness scoring detects hallucinations by checking claims against context
• LLM-as-judge is practical for automated evaluation at scale
• Run evaluation on every change — treat quality as a CI/CD gate

Hands-On Labs

1. Evaluate Retrieval Quality

   Measure precision, recall, and MRR on a test dataset.

   30 min - Intermediate

   • Create a test dataset with queries and expected relevant documents
   • Run retrieval and compute precision@5, recall@5, MRR
   • Compare metrics across different retrieval strategies
   • Identify queries where retrieval fails

   View lab files on GitHub

2. Build a Hallucination Detection Pipeline

   Detect and score hallucinations automatically.

   35 min - Advanced

   • Implement LLM-as-judge for groundedness scoring
   • Run on 50 query-answer pairs
   • Build a Grafana dashboard for quality metrics
   • Set up alerts for quality threshold violations

   View lab files on GitHub