Lecture 33: RAG Evaluation Metrics - Measure & Improve System Accuracy

Description

Master RAG system evaluation! Learn confusion matrices, precision, recall, F1 scores, human evaluation, LLM-based assessment, and practical metrics to measure RAG accuracy and quality. 🎯 What You'll Learn: • Confusion matrix fundamentals • Accuracy, Precision, Recall, F1 Score calculations • Class imbalance and evaluation challenges • Human evaluation for RAG systems • LLM as a judge for automated evaluation • Reference-free evaluation frameworks • Key RAG evaluation metrics (Faithfulness, Relevance) • RAGAS library for comprehensive evaluation • Context precision and recall • Groundedness and answer relevancy measurement • Fluency and completeness assessment • Practical evaluation strategies 💡 Key Topics Covered: ✅ Confusion Matrix: True Positive, False Positive, True Negative, False Negative ✅ Accuracy calculation and interpretation ✅ Precision: Positive prediction accuracy ✅ Recall: Coverage of actual positives ✅ F1 Score: Balanced metric for imbalanced datasets ✅ Class imbalance problems and solutions ✅ Human evaluation methodologies ✅ LLM-based automated evaluation ✅ Faithfulness: Claims supported by context ✅ Relevance: Answer relevance to question ✅ Context Precision: Fraction of relevant context ✅ Context Recall: Coverage of ground truth ✅ RAGAS library metrics ✅ Evaluation framework selection 📚 Practical Examples: • Building confusion matrices from classification results • Calculating metrics for spam email detection • Imbalanced dataset evaluation (cancer detection) • Human evaluation workflows • Rating answer quality (relevance, faithfulness, completeness) • LLM-based evaluation prompts • Context precision & recall calculations • RAGAS library implementation • Multi-metric evaluation strategy • Real-world RAG system assessment 🛠️ Tools & Libraries: Python • Scikit-learn • RAGAS Library • Pandas • NumPy LLM Evaluation Frameworks • Confusion Matrix Tools Evaluation Metrics • AutomatedEvaluation Tools Perfect for building production-grade RAG systems with measurable quality assurance! #GenAI #RAGEvaluation #Metrics #MachineLearning #ConfusionMatrix #Precision #Recall #F1Score #DataScience #Python