Sheila A. Wallace

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.

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.

We assessed the effects of vaginal lasers for treating stress urinary incontinence (SUI) in women. This systematic review and meta-analysis included randomized trials of women with SUI and assessed therapy with vaginal laser vs sham, control, or topical treatments. Nine studies of 689 women with SUI were included. Overall, the data regarding vaginal laser vs sham treatments were uncertain, and few studies assessed outcome beyond 1 year. In the short term, there may be no difference between the number of continent women between women who underwent vaginal laser compared with sham (risk ratio, 1.50; 95% CI, 0.72-3.10; I² = 81%; n = 196; very low-certainty evidence). Although vaginal lasers may improve patient-reported incontinence measures compared with sham, the point estimate for improvement does not meet the prespecified minimally clinical importance differences (mean difference, -1.42 points; 95% CI, -2.41 to -0.43; I² = 54%; n = 632; very low-certainty evidence). Only 1 study provided evidence comparing vaginal laser with topical estrogen. There were no major adverse events in any treatment group at any time point. Vaginal lasers may have little to no effect on SUI as measured by both clinical assessment in the short term compared with sham, control, or topical treatments, but the evidence is uncertain. Vaginal lasers may improve patient-reported measures of SUI, but the evidence is uncertain, and the change may not be noticeable to patients. No major adverse events were reported.

Introduction: Information retrieval is essential for evidence synthesis, but developing search strategies can be labor‐intensive and time‐consuming. Automating these processes would be of benefit and interest, though it is unclear if Information Specialists (IS) are willing to adopt artificial intelligence (AI) methodologies or how they currently use them. In January 2025, the NIHR Innovation Observatory and NIHR Methodology Incubator for Applied Health and Care Research co‐sponsored the inaugural CORE Information Retrieval Forum, where attendees discussed AI's role in information retrieval. Methods: The CORE Information Retrieval Forum hosted a Knowledge Café. Participation was voluntary, and attendees could choose one of six event‐themed discussion tables including AI. To support each discussion, a QR code linking to a virtual collaboration tool (Padlet; padlet.com ) and a poster in the exhibition space were available throughout the day for attendee contributions. Results: The CORE Information Retrieval Forum was attended by 131 IS from nine different types of organizations, with most from the UK and ten countries represented overall. Among the six discussion points available in the Knowledge Café, the AI table was the most popular, receiving the highest number of contributions ( n = 49). Following the Forum, contributions to the AI topic were categorized into four themes: critical perception ( n = 21), current uses ( n = 19), specific tools ( n = 2), and training wants/needs ( n = 7). Conclusions: While there are critical perspectives on the integration of AI in the IS space, this is not due to a reluctance to adapt and adopt but from a need for structure, education, training, ethical guidance, and systems to support the responsible use and transparency of AI. There is interest in automating repetitive and time‐consuming tasks, but attendees reported a lack of appropriate supporting tools. More work is required to identify the suitability of currently available tools and their potential to complement the work conducted by IS.