ReviewPatterns (New York, N.Y.)2025
Unleashing the potential of prompt engineering for large language models.
Review in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
74 citing papers in PubMed.
- Comparing the performance of a large language model and naive human interviewers in interviewing children about a witnessed mock-event.PloS one · 2025Trial
- Prompt Engineering of Large Language Models for Medication Dose Calculation.Clinical pharmacology and therapeutics · 2026Article
- Multimodal large language models for bladder tumor detection in cystoscopy: a retrospective benchmarking study.World journal of urology · 2026Article
- MetaboAnnotate: An AI-powered Multiagent Framework for Integrating Annotation Tools for Untargeted Metabolomics.Analytical chemistry · 2026Article
- Large Language Models for Heterogeneous Data Mining in Liver Disease: Framework Development and Retrospective Validation Study.Journal of medical Internet research · 2026Article
- Large language model prompt engineering for medical education: A practical guide for the Australian and New Zealand College of Anaesthetists Final Examination.Anaesthesia and intensive care · 2026Article
- Evaluating large language models for lay summaries of radiology reports using tailored prompting strategies and mixed-method assessment.PLOS digital health · 2026Article
- Large Language Models for Patient Education in Cardiovascular Imaging: Prospective Observational Comparative Study.JMIR formative research · 2026Observational
- Prompt Framing and Evidence Requirement for AI-Generated Educational Responses in Dental Education: Experimental Study.JMIR medical education · 2026Article
- Performance of multimodal large language models on image-based surgical anatomy, anatomical pathology, and radiology questions.Anatomical sciences education · 2026Article
- SmileyLlama: modifying large language models for directed chemical space exploration.Nature computational science · 2026Article
- Biomedical Large Language Models and Prompt Engineering for Causality Assessment of Individual Case Safety Reports in Pharmacovigilance.Pharmaceutical research · 2026Article
- From API to Action: A Multi-Model Comparison of OpenAI, Anthropic, Google, and Meta LLMs for Clinical Trial Data Extraction.Bioengineering (Basel, Switzerland) · 2026Article
- Clinical drug report generation using multi-phase prompt large language models.Scientific reports · 2026Article
- Prompt sensitivity of large language models in orthodontic patient counseling: a scenario-based experimental study.BMC oral health · 2026Article
- Evaluating Large Language Models for Post-Publication Promotion: A Blinded Comparative Study of Social Media Posts in Public Health.Journal of Korean medical science · 2026Article
- Culturally-Aware Prompting in conversational AI: supporting perceived communicative effectiveness in cross-cultural teams in Australia.Scientific reports · 2026Article
- CistromeMeta: a large language model powered tool for automated ChIP-seq metadata extraction.Bioinformatics (Oxford, England) · 2026Article
- Prompt Architecture as a High-Impact Design Factor in Expert-Rated Clinical Documentation Quality: A Controlled Comparative Study in Inpatient Rehabilitation.Bioengineering (Basel, Switzerland) · 2026Article
- Plain Language Summarization of Environmental Health Research Using Generative AI: Community-Engaged Qualitative Study.Journal of medical Internet research · 2026Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review explores the role of prompt engineering in unleashing the capabilities of large language models (LLMs). Prompt engineering is the process of structuring inputs, and it has emerged as a crucial technique for maximizing the utility and accuracy of these models. Both foundational and advanced prompt engineering methodologies-including techniques such as self-consistency, chain of thought, and generated knowledge, which can significantly enhance the performance of models-are explored in this paper. Additionally, the prompt methods for vision language models (VLMs) are examined in detail. Prompt methods are evaluated with subjective and objective metrics, ensuring a robust analysis of their efficacy. Critical to this discussion is the role of prompt engineering in artificial intelligence (AI) security, particularly in terms of defending against adversarial attacks that exploit vulnerabilities in LLMs. Strategies for minimizing these risks and improving the robustness of models are thoroughly reviewed. Finally, we provide a perspective for future research and applications.
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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.