{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Tech Stories Tech Brief By HackerNoon","title":"What Production-Grade RAG Evaluation Should Look Like","html":"","width":"100%","height":180,"duration":2100,"description":"\n This story was originally published on HackerNoon at: https://hackernoon.com/what-production-grade-rag-evaluation-should-look-like.\nLearn how to evaluate agentic RAG systems using RAGAS, LangSmith, Langfuse, critic scores, retrieval behavior, latency, and cost.\nCheck more stories related to tech-stories at: https://hackernoon.com/c/tech-stories.\n You can also check exclusive content about #agentic-rag, #ai-evaluation, #ai-observability, #retrieval-evaluation, #llm-as-a-judge, #rag-faithfulness-scores, #corrective-rag, #hackernoon-top-story, and more.\nThis story was written by: @tnawaz. Learn more about this writer by checking @tnawaz's about page,\n and for more stories, please visit hackernoon.com.\nThis article argues that evaluating agentic RAG systems requires far more than a single faithfulness score. It explores a production-focused evaluation stack built around RAGAS component metrics, node-level observability with LangSmith and Langfuse, critic scoring, retrieval-round analysis, latency and cost monitoring, and carefully curated evaluation datasets. The central thesis is that modern RAG systems fail in many ways that end-to-end metrics alone cannot detect.\n \n ","thumbnail_url":"https://img.transistorcdn.com/IuqXIpaNNuezY7jNfIDnL5gqB1iL_SEndwUUzLGdljY/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxNDI5LzE2ODM1/ODM0NjQtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}