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25 lines
1.8 KiB
Markdown
25 lines
1.8 KiB
Markdown
# LLM Output Truncation Research
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A structured analysis of why large language models produce incomplete outputs, and documented methods to restore full-fidelity generation. All findings are drawn from controlled experiments, published studies, and field-tested engineering practices.
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## Directory Structure
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### Root Causes
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Analysis of the economic, architectural, and behavioral mechanisms that drive output truncation in production LLMs.
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- [RLHF and Compute Economics](root-causes/rlhf-and-compute.md) — How reinforcement learning and cost optimization create systematic brevity bias.
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- [Training Data Bias](root-causes/training-data-bias.md) — How placeholder patterns in human-written code propagate into model outputs.
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- [Cognitive Shortcuts](root-causes/cognitive-shortcuts.md) — Empirical evidence of models taking shortcuts on complex or lengthy tasks.
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- [Output Limits](root-causes/output-limits.md) — Context window asymmetry and consumer-tier truncation mechanisms.
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### Remediation
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Documented techniques for overriding default truncation behavior, ordered from parameter-level fixes to full architectural solutions.
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- [Parameter Tuning](remediation/parameter-tuning.md) — Temperature, Top-p, and Gemini thinking-level configuration.
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- [Prompt Engineering](remediation/prompt-engineering.md) — Structural prompt techniques: syntax binding, XML frameworks, and verification loops.
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- [Architectural Patterns](remediation/architectural-patterns.md) — MCP integration, lazy-loaded skills, and developer platform access.
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- [Reference Prompts](remediation/reference-prompts.md) — Ready-to-use prompt templates for enforcing complete outputs.
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### Findings
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- [Empirical Results](findings/empirical-results.md) — Controlled experiment data from 2025 academic studies.
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- [References](findings/references.md) — Cited studies and further reading.
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