Information Specialist Roles in the Era of Large Language Models: Prompting Continued Professional Development.

IF 3.8 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Campbell Systematic Reviews Pub Date : 2026-05-11 eCollection Date: 2026-06-01 DOI:10.1177/18911803261449731
Hannah O'Keefe, Claire H Eastaugh, Sheila A Wallace, Fiona R Beyer
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Abstract

Prompt engineering is the formation of queries or instructions (prompts) that are deployed in large language models. These prompts are often underscored by frameworks, designed to give structure and encourage robust answers. Discussions in recent information specialists' networks and events have highlighted on multiple occasions that information specialists are well placed to undertake prompt engineering tasks. However, there is little published information outlining why and how information specialists are best placed for these tasks and the universal understanding between information specialists has not filtered out to the wider research synthesis community so progress in this area is slow. Here, we discuss the parallels between information specialist tasks and large language model engineering tasks and demonstrate that the parallels run deeper than just prompts. There are strong similarities between information retrieval and context engineering, prompt engineering and vibing. In the briefest sense, we can consider context engineering to be like a search platform, prompt engineering like a structured search strategy, and vibe coding like a search engine input. Knowledge sharing and dissemination of these core concepts amongst information specialists and research synthesists will drive methods development, particularly with the rise of large language models in synthesis automation, give potential for continual professional development courses and e-learning to be developed, and expand the roles of information specialists. To initiate progress in this area, we discuss the anticipated future direction of information specialist roles.

大语言模型时代的信息专家角色:促进持续的专业发展。
提示工程是部署在大型语言模型中的查询或指令(提示)的形成。这些提示通常由框架强调,旨在给出结构并鼓励有力的答案。在最近的信息专家网络和事件的讨论中,多次强调了信息专家能够很好地承担即时的工程任务。然而,几乎没有公开的信息概述信息专家为什么以及如何最适合这些任务,信息专家之间的普遍理解还没有过滤到更广泛的研究综合社区,因此这一领域的进展缓慢。在这里,我们将讨论信息专家任务和大型语言模型工程任务之间的相似之处,并演示它们之间的相似之处远不止提示。信息检索与上下文工程、提示工程和振动有很强的相似性。简而言之,我们可以将上下文工程视为搜索平台,将提示工程视为结构化搜索策略,而将氛围编码视为搜索引擎输入。这些核心概念在信息专家和研究综合人员之间的知识共享和传播将推动方法的发展,特别是随着综合自动化中大型语言模型的兴起,为持续的专业发展课程和电子学习的发展提供潜力,并扩大信息专家的作用。为了在这一领域取得进展,我们讨论了信息专家角色的预期未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Campbell Systematic Reviews
Campbell Systematic Reviews Social Sciences-Social Sciences (all)
CiteScore
5.50
自引率
21.90%
发文量
80
审稿时长
6 weeks
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