{"podcast":{"title":"Tech Stories Tech Brief By HackerNoon","slug":"tech-stories-tech-brief-by-hackernoon-6365648","podcast_index_feed_id":6365648,"rss_url":"https://feeds.transistor.fm/tech-stories-tech-brief-by-hackernoon","website_url":"https://hackernoon.com/c/tech-stories","image_url":"https://img.transistorcdn.com/P-42oHG33sV1aPenbVg2DrwaV5AQtWp46GJ4Bp_EP-s/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxNDI5LzE2ODM1/ODM0NjQtYXJ0d29y/ay5qcGc.jpg","author":"HackerNoon","episode_count":100,"summary":"Learn the latest tech-stories updates in the tech world.","last_synced_at":"2026-07-30T06:17:37.489609+00:00","page_url":"https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648"},"episode":{"title":"What Production-Grade RAG Evaluation Should Look Like","slug":"what-production-grade-rag-evaluation-should-look-like","published_at":"2026-06-02T16:00:47+00:00","page_url":"https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648/what-production-grade-rag-evaluation-should-look-like","show_page_url":"https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648","url":"https://share.transistor.fm/s/2693f1ba","audio_url":"https://media.transistor.fm/2693f1ba/f96ce82b.mp3","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.","meta_description":"This story was originally published on HackerNoon at: https://hackernoon.com/what-production-grade-rag-evaluation-should-look-like . Learn how to evaluate…","key_points":[],"chapters":[],"topics":[],"duration_seconds":2100,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/tech-stories-tech-brief-by-hackernoon-6365648/episodes/what-production-grade-rag-evaluation-should-look-like/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648/what-production-grade-rag-evaluation-should-look-like.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}