Evaluate RAG system performance

About this Video

• Video Title: Evaluate RAG system performance
• Channel: Gooru Content
• Speakers: None specified in transcript
• Duration: 00:04:16

Introduction

This video details creating a comprehensive evaluation framework for Retrieval Augmented Generation (RAG) systems. It emphasizes moving beyond basic functional tests to in-depth performance analysis, focusing on ensuring reliability and accuracy by evaluating both retrieval and generation components.

Key Takeaways

• Defining Success: Clearly defining goals and metrics (groundedness, relevance, completeness, latency) is crucial for effective RAG system evaluation.
• High-Quality Evaluation Data: A representative dataset including diverse queries (simple, complex, ambiguous, out-of-scope), human-curated answers, and relevant document IDs is essential for accurate assessment.
• Retrieval Component Evaluation: Metrics like recall@k, precision@k, and mean reciprocal rank (MRR) are used to evaluate the retrieval component's effectiveness in identifying and ranking relevant documents.
• Generated Answer Evaluation: Evaluation criteria for generated answers include groundedness, relevance, coherence, fluency, and completeness.
• AI Model Assistance: AI models can assist in evaluation by acting as judges for relevance, completeness, and hallucination detection.