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Predicting LLM Performance from Prompt Linguistic Features: An Empirical Study in Requirements Engineering
Background. LLM outputs are highly sensitive to prompt formulation: small wording changes can substantially affect output quality. This matters in software engineering, where prompts guide requirements analysis, code generation, and artefact synthesis. Poor formulations yield unreliable artefacts, y
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