Context‐engineering large language models for scalable structured metadata extraction in ecological literature reviews

Hundreds of thousands of peer‐reviewed articles and grey‐literature reports appear each year in ecology and conservation, creating a pressing need for scalable synthesis tools. We demonstrate that transformer‐based Large Language Models (LLMs), when embedded within a practical, expert‐in‐the‐loop pipeline, offer a reliable path forward requiring no deep AI expertise. In this study, we benchmark nine high ranking LLMs (five standard and four reasoning‐enhanced) across four context‐aware prompting strategies from Basic zero‐shot to advanced Directive formats to extract structured fields (study location, biome, taxa) from abstracts. Our results reveal that while certain models emerge as top performers across the tested fields, no single LLM dominates every task. However, context‐rich prompting drives gains across all models. By mapping API latency, token costs and F 1 score, we show how researchers can select the optimal model–prompt pairing for their speed, budget or performance needs. We also provide actionable guidelines for effective context engineering in prompts and validation workflows that build trust in model outputs. To lower the barrier to entry, we also introduce LitStructLLM, a cross‐platform desktop application that lets users upload CSV or PDF inputs, tailor prompts and export reliable, schema‐compliant outputs without writing code. This work underscores the synergy of human expertise and LLM capabilities, offering a robust, transparent pipeline for efficient, large‐scale literature mining in ecology and conservation.