Building Trustworthy Production RAG Systems Through Continuous Evaluation

A guide details building an evaluation pipeline for RAG systems to catch retrieval failures, hallucinations, and performance drift. It starts with a golden dataset of 20-30 questions with known answers and source documents. RAGAS automates scoring on context precision, context recall, faithfulness, and answer relevancy. Custom LLM-as-judge prompts catch domain-specific failures like stale sources.