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prompt-drift

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This project presents a reproducible framework for analyzing instruction-following stability in large language models under prompt drift. I evaluate how controlled prompt variations impact instruction adherence, format correctness, and semantic consistency, supported by standardized evaluation protocols and auditable artifacts.

  • Updated Jan 7, 2026
  • Python

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