Equation 14 · Ten Failure Modes That Define Production RAG
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and d grows with corpus redundancy in a way that is invisible to any retrieval metric computed against a deduplicated relevance-judgement set, which is how most benchmarks are built. Schelpe’s recent empirical analysis, released as a preprint and not yet peer reviewed, measured exact-duplication rates across three regimes and found them to vary enormously by domain — under one percent in academic retrieval corpora, roughly a quarter of retrieved content in an enterprise document setting, and above four-fifths of retrieved content in a conversational-agent setting — while finding, across evaluation with several large language model providers, that removing exact duplicates before generation…
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and d grows with corpus redundancy in a way that is invisible to any retrieval metric computed against a deduplicated relevance-judgement set, which is how most benchmarks are built. Schelpe’s recent empirical analysis, released as a preprint and not yet peer reviewed, measured exact-duplication rates across three regimes and found them to vary enormously by domain — under one percent in academic retrieval corpora, roughly a quarter of retrieved content in an enterprise document setting, and above four-fifths of retrieved content in a conversational-agent setting — while finding, across evaluation with several large language model providers, that removing exact duplicates before generation produced no measurable quality regression [ 12 ] . Read as a preliminary but suggestive result rather than a settled finding, it argues that deduplication is close to a free win precisely because the redundant slots were never doing any work.
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