RAG Evaluation Framework

Description

RAG Evaluation Framework: Metrics, Code & Production Monitoring A comprehensive deep dive into evaluating Retrieval Augmented Generation systems in production. Covers the core metrics for retrieval and generation quality, LLM-as-Judge implementation for faithfulness scoring, and real-world architecture for continuous monitoring with async evaluation pipelines. ================ What you will learn: ================ The Core Problem - RAG systems fail silently: retrieval or generation, evaluation tells you which broke - Silent degradation as knowledge base grows and query patterns evolve - Without metrics you are guessing, with metrics you are debugging systematically Retrieval Metrics - Context Precision: percentage of retrieved chunks that are actually useful - Context Recall: percentage of relevant chunks successfully retrieved - Mean Reciprocal Rank (MRR): how early the most relevant chunk appears in results Generation Metrics - Faithfulness: every claim traces back to retrieved context, no hallucination (reference-free) - Answer Relevance: response addresses the actual question (reference-free) - Answer Correctness: output matches ground truth semantically (requires labels) Building Your Test Set - Create question, answer, and relevant chunk ID triplets from real queries - Include edge cases: ambiguous questions, multi-hop reasoning, out-of-scope queries - Evolve test set as knowledge base and user patterns change Faithfulness Check with LLM-as-Judge - Extract atomic claims (single indivisible facts) from the answer - Classify each claim as supported or unsupported by context - Score = supported claims / total claims - Production target: 0.85 to 0.95 depending on use case Retrieval Metrics Calculation - Precision = true positives / total retrieved - Recall = true positives / total relevant in corpus - Reciprocal Rank = 1 / position of first relevant chunk - MRR = average reciprocal rank across test set End-to-End Evaluation Pipeline - Test dataset feeds questions through RAG pipeline - Separate evaluators for retrieval and generation metrics - Dashboard surfaces regressions before production impact Production Monitoring Architecture - Sample 1-5% of production traffic, evaluate offline - Async evaluation keeps latency out of user path - Threshold-based alerts when faithfulness or relevance drops - Data flywheel: flagged samples feed back into test dataset =========== Timestamps: =========== 00:00 - Introduction: Why RAG Systems Fail Silently 00:24 - The Core Problem: Retrieval vs Generation Failures 01:32 - Retrieval Metrics: Precision, Recall, MRR 02:48 - Generation Metrics: Faithfulness, Relevance, Correctness 03:49 - Building Your Evaluation Test Set 04:47 - Step 2: Code Examples 04:52 - Code: Faithfulness Check with LLM-as-Judge 06:06 - Code: Retrieval Metrics Calculation 07:04 - Step 3: Real-World Architecture 07:14 - End-to-End Evaluation Pipeline 08:04 - Production Monitoring Architecture 09:10 - Key Takeaways and Closing ========= About me: ========= I'm Mukul Raina, a Senior Software Engineer and Tech Lead at Microsoft, with a Master's in Computer Science from the University of Oxford, UK #RAG #RAGEvaluation #ProductionAI #LLMasJudge #Faithfulness #RetrievalMetrics #AIArchitecture #RetrievalAugmentedGeneration #MLOps #AIEngineering #SystemDesign