<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>AI Quality on Bharat Singh</title><link>https://bsbhadwal.com/tags/ai-quality/</link><description>Recent content in AI Quality on Bharat Singh</description><image><title>Bharat Singh</title><url>https://bsbhadwal.com/images/bharat-singh-ai-systems-architect.jpg</url><link>https://bsbhadwal.com/images/bharat-singh-ai-systems-architect.jpg</link></image><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 15 Jun 2026 00:00:00 +0530</lastBuildDate><atom:link href="https://bsbhadwal.com/tags/ai-quality/index.xml" rel="self" type="application/rss+xml"/><item><title>Evaluating RAG Without Ground Truth: Production Pitfalls of LLM-as-a-Judge</title><link>https://bsbhadwal.com/posts/evaluating-rag-without-ground-truth-llm-as-a-judge-pitfalls/</link><pubDate>Mon, 15 Jun 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/evaluating-rag-without-ground-truth-llm-as-a-judge-pitfalls/</guid><description>Production analysis of evaluating enterprise RAG pipelines without human ground truth, mitigating LLM-as-a-judge bias, and building empirical synthetic test sets.</description></item></channel></rss>