The paper introduces a reproducible optimization protocol for prompt-based LLM workflows in evidence synthesis that separates task definitions from prompt harnesses, optimizes the harness against metrics and examples, and preserves the result as an inspectable artefact.
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A Reproducible Optimisation Protocol for Calibrating Prompt-Based Large Language Model Workflows in Evidence Synthesis
The paper introduces a reproducible optimization protocol for prompt-based LLM workflows in evidence synthesis that separates task definitions from prompt harnesses, optimizes the harness against metrics and examples, and preserves the result as an inspectable artefact.