ArticleMethodsX2026
A reproducible optimisation protocol for calibrating prompt-based large language model workflows in evidence synthesis.
Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This methods article presents a reproducible workflow for optimising and testing improving large language models (LLMs) prompts in evidence-synthesis tasks with defined inputs and outputs. The method separates the fixed rules that define the scientific task from the editable prompt instructions that frame and apply them. It optimises those instructions against labelled or reference examples and an explicit task metric, then saves the final workflow with its specification, metric, settings, and evaluation traces so others can inspect and reuse it. The example code uses DSPy and GEPA tools, but the same logic can transfer to other prompt-optimisation frameworks that support structured task definitions, metric-guided search, and reusable outputs. Title and abstract screening is the illustrative validation case here as it provides labelled benchmark data and clear evaluation metrics. The demonstrated workflow uses a smaller
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