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    <title>Index-Tuning on Kent Yao</title>
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      <title>When the Index Tuner&#39;s Cost Model Lies: Where LLMs See What DTA Can&#39;t</title>
      <link>https://yaooqinn.github.io/posts/query-engines/llm-index-tuning-vs-dta/</link>
      <pubDate>Sat, 30 May 2026 00:00:00 +0000</pubDate>
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      <description>A Microsoft team evaluates LLM-driven index tuning on real enterprise customer workloads. On query 22 of Real-R, the SOTA commercial tuner DTA recommends indexes that cause a near-10x regression; on the same query, GPT-5 cuts execution time from 10 seconds to 4. The LLM wins precisely where the what-if cost model is wrong. But that intuition is high-variance, can&amp;rsquo;t be bolted into the existing architecture, and can&amp;rsquo;t be validated cheaply — it&amp;rsquo;s not a replacement for DTA today, it&amp;rsquo;s a source of the candidate indexes DTA can&amp;rsquo;t see.</description>
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