Episode
What Production-Grade RAG Evaluation Should Look Like
- Published
- Jun 2, 2026
- Duration seconds
- 2100
- Processing state
not_requested- Canonical source
- https://share.transistor.fm/s/2693f1ba
Actions
POST https://stenobird.com/v1/public/podcasts/tech-stories-tech-brief-by-hackernoon-6365648/episodes/what-production-grade-rag-evaluation-should-look-like/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648/what-production-grade-rag-evaluation-should-look-like.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
This story was originally published on HackerNoon at: https://hackernoon.com/what-production-grade-rag-evaluation-should-look-like . Learn how to evaluate agentic RAG systems using RAGAS, LangSmith, Langfuse, critic scores, retrieval behavior, latency, and cost. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories . 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. This story was written by: @tnawaz . Learn more about this writer by checking @tnawaz's about page, and for more stories, please visit hackernoon.com . This 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.