Building Trustworthy Production RAG Systems Through Continuous Evaluation

Building trustworthy production RAG systems requires a continuous evaluation workflow to catch silent failures like incorrect retrieval or hallucinations. The process begins with creating a "golden dataset" of questions and known correct answers, followed by an initial manual review. For automated scoring, the RAGAS library can measure metrics like context precision and faithfulness, but it has limitations in assessing domain-specific correctness. To overcome these limitations, a custom LLM judge can be implemented with specific prompts, and a human-in-the-loop process is essential for reviewing disagreements and monitoring for performance drift over time.