ArticleJournal of the Medical Library Association : JMLA2025
Integrating PICO principles into generative artificial intelligence prompt engineering to enhance information retrieval for medical librarians.
Article in Journal of the Medical Library Association : JMLA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- How ChatGPT writes scientific titles in medical research: structural and content differences compared to human authors.Journal of the Medical Library Association : JMLA · 2026Article
- Comparing five generative AI chatbots' answers to LLM-generated clinical questions with medical information scientists' evidence summaries.Journal of the Medical Library Association : JMLA · 2026Article
- Comparing Five Generative AI Chatbots' Answers to LLM-Generated Clinical Questions with Medical Information Scientists' Evidence Summaries.medRxiv : the preprint server for health sciences · 2025Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prompt engineering, an emergent discipline at the intersection of Generative Artificial Intelligence (GAI), library science, and user experience design, presents an opportunity to enhance the quality and precision of information retrieval. An innovative approach applies the widely understood PICO framework, traditionally used in evidence-based medicine, to the art of prompt engineering. This approach is illustrated using the "Task, Context, Example, Persona, Format, Tone" (TCEPFT) prompt framework as an example. TCEPFT lends itself to a systematic methodology by incorporating elements of task specificity, contextual relevance, pertinent examples, personalization, formatting, and tonal appropriateness in a prompt design tailored to the desired outcome. Frameworks like TCEPFT offer substantial opportunities for librarians and information professionals to streamline prompt engineering and refine iterative processes. This practice can help information professionals produce consistent and high-quality outputs. Library professionals must embrace a renewed curiosity and develop expertise in prompt engineering to stay ahead in the digital information landscape and maintain their position at the forefront of the sector.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.