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    <title>S. BECONIS — Writing</title>
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    <description>Technical writing by Saulius Beconis.</description>
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      <title>How to evaluate a RAG system without fooling yourself</title>
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      <description>How to evaluate RAG honestly — a test set from real questions, retrieval measured separately from generation, and the usual self-deceptions avoided.</description>
      <pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate>
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      <title>Hybrid search and reranking, measured</title>
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      <description>BM25, dense, hybrid and reranked retrieval measured on three BEIR datasets. When the textbook RAG pipeline pays for its latency and when a single embedding model wins.</description>
      <pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate>
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      <title>The cheapest way to trust AI document extraction</title>
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      <description>Measured on 200 real receipts — how plain-code validation decides which AI-extracted documents can skip human review, and what it still misses.</description>
      <pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate>
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    <item>
      <title>When not to use an LLM</title>
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      <description>A practical decision test for engineers and buyers — when a language model is the right component, and when code, search or a human is cheaper and more reliable.</description>
      <pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate>
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      <title>Why RAG retrieval fails</title>
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      <description>Most bad RAG answers start with bad retrieval. A field guide to the failure modes — parsing, chunking, vocabulary mismatch, ranking depth — and how to diagnose them.</description>
      <pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate>
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