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What Production-Grade RAG Evaluation Should Look Like
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.
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By HackerNoonThis 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.