# Compiling Prompts, Not Crafting Them: A Reproducible Workflow for AI-Assisted Evidence Synthesis

## TL;DR

This paper introduces a reproducible, structured workflow for AI-assisted systematic literature reviews that replaces manual prompt crafting with automated prompt optimization, enhancing reliability and transparency.

## Contribution

It adapts declarative prompt optimisation techniques for SLR automation, providing a domain-specific framework with code for verifiable, transparent LLM pipelines.

## Key findings

- Demonstrates applicability of prompt optimisation to SLR
- Provides a reproducible blueprint with code examples
- Enhances transparency and reliability in evidence synthesis

## Abstract

Large language models (LLMs) offer significant potential to accelerate systematic literature reviews (SLRs), yet current approaches often rely on brittle, manually crafted prompts that compromise reliability and reproducibility. This fragility undermines scientific confidence in LLM-assisted evidence synthesis. In response, this work adapts recent advances in declarative prompt optimisation, developed for general-purpose LLM applications, and demonstrates their applicability to the domain of SLR automation. This research proposes a structured, domain-specific framework that embeds task declarations, test suites, and automated prompt tuning into a reproducible SLR workflow. These emerging methods are translated into a concrete blueprint with working code examples, enabling researchers to construct verifiable LLM pipelines that align with established principles of transparency and rigour in evidence synthesis. This is a novel application of such approaches to SLR pipelines.