{
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  "slug": "structured-configuration-style-prompts-as-variance-control-an-analytical-framewo",
  "title": "Structured Configuration-Style Prompts as Variance Control: An Analytical Framework for Cross-LLM Output Variance Reduction",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Large language models (LLMs) are increasingly used as interchangeable components in automated pipelines, yet the same natural-language instruction produces materially different outputs across models, sampling seeds, and runs. This variance blocks the use of LLMs where reproducible execution matters: batch document processing, schema-constrained data extraction, and multi-step agentic workflows. The specific gap is that prompt engineering is practiced as a literary craft with no predictive model of how prompt form controls output variance, and the largest existing variance decomposition of LLM creative output attributes 36.43% of originality variance to prompts but does not separate prompt form from prompt content. We develop an analytical framework that treats a prompt as a partial program whose unspecified parameters are filled in by each model's interpretation, models interpretation divergence as an input to the output-variance machinery of sensitivity analysis, and derives a variance-reduction factor equal to the share of interpretation variance carried by the parameters a structured prompt pins down. Under explicitly labeled illustrative assumptions, pinning four of six interpretation dimensions removes 82% of the prompt-attributable interpretation variance, bounded by the empirical decomposition to at most 29.9 percentage points of total variance; a 20-field schema at an assumed per-field compliance of 0.986 yields 75.4% full-compliance probability against 12.2% for an assumed prose baseline of 0.90 per field, a 3.57-fold reduction in violation probability. The framework converts prompt engineering into a specification discipline with measurable design targets and a falsifiable pilot protocol of 53 paired tasks.",
  "abstract_markdown": "Large language models (LLMs) are increasingly used as interchangeable components in automated pipelines, yet the same natural-language instruction produces materially different outputs across models, sampling seeds, and runs. This variance blocks the use of LLMs where reproducible execution matters: batch document processing, schema-constrained data extraction, and multi-step agentic workflows. The specific gap is that prompt engineering is practiced as a literary craft with no predictive model of how prompt form controls output variance, and the largest existing variance decomposition of LLM creative output attributes 36.43% of originality variance to prompts but does not separate prompt form from prompt content. We develop an analytical framework that treats a prompt as a partial program whose unspecified parameters are filled in by each model's interpretation, models interpretation divergence as an input to the output-variance machinery of sensitivity analysis, and derives a variance-reduction factor equal to the share of interpretation variance carried by the parameters a structured prompt pins down. Under explicitly labeled illustrative assumptions, pinning four of six interpretation dimensions removes 82% of the prompt-attributable interpretation variance, bounded by the empirical decomposition to at most 29.9 percentage points of total variance; a 20-field schema at an assumed per-field compliance of 0.986 yields 75.4% full-compliance probability against 12.2% for an assumed prose baseline of 0.90 per field, a 3.57-fold reduction in violation probability. The framework converts prompt engineering into a specification discipline with measurable design targets and a falsifiable pilot protocol of 53 paired tasks.",
  "date": "2026-11-13",
  "first_published": "2026-11-13",
  "last_revised": "2026-11-13T00:00:00Z",
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  "doi": null,
  "doi_url": null,
  "url": "https://papers.qnfo.org/papers/structured-configuration-style-prompts-as-variance-control-an-analytical-framewo",
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        "name": "Rowan Brad Quni-Gudzinas",
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    "titles": [
      {
        "title": "Structured Configuration-Style Prompts as Variance Control: An Analytical Framework for Cross-LLM Output Variance Reduction"
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