Results 11 to 20 of about 370,431 (273)

Prompt Engineering Paradigms for Medical Applications: Scoping Review

open access: yesJournal of Medical Internet Research
BackgroundPrompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain
Jamil Zaghir   +5 more
doaj   +4 more sources

Review of large vision models and visual prompt engineering

open access: yesMeta-Radiology, 2023
Visual prompt engineering is a fundamental methodology in the field of visual and image artificial general intelligence. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident.
Jiaqi Wang   +20 more
doaj   +3 more sources

Prompt Engineering in Medical Education

open access: yesInternational Medical Education, 2023
Artificial intelligence-powered generative language models (GLMs), such as ChatGPT, Perplexity AI, and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate ...
Thomas F. Heston, Charya Khun
doaj   +3 more sources

Green prompt engineering for sustainable generative AI [PDF]

open access: yesEnvironmental Science and Ecotechnology
Prompt engineering involves manual design and optimization of text-based instructions or queries, enabling precise control over outputs generated by pre-trained large language models (LLMs) and ensuring alignment with desired responses.
Sanjay Podder, Hema Date, Shankar Murthy
doaj   +2 more sources

Prompt Engineering in Clinical Practice: Tutorial for Clinicians

open access: yesJournal of Medical Internet Research
Large language models (LLMs), such as OpenAI’s GPT series and Google’s PaLM, are transforming health care by improving clinical decision-making, enhancing patient communication, and simplifying administrative tasks.
Jialin Liu   +3 more
doaj   +2 more sources

AI literacy and its implications for prompt engineering strategies

open access: yesComputers and Education: Artificial Intelligence
Artificial intelligence technologies are rapidly advancing. As part of this development, large language models (LLMs) are increasingly being used when humans interact with systems based on artificial intelligence (AI), posing both new opportunities and ...
Nils Knoth   +3 more
doaj   +3 more sources

Integrating PICO principles into generative artificial intelligence prompt engineering to enhance information retrieval for medical librarians [PDF]

open access: yesJournal of the Medical Library Association
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.
Kyle Robinson   +2 more
doaj   +2 more sources

Prompt engineering for digital mental health: a short review [PDF]

open access: yesFrontiers in Digital Health
Prompt engineering, the process of arranging input or prompts given to a large language model to guide it in producing desired outputs, is an emerging field of research that shapes how these models understand tasks, process information, and generate ...
Y. H. P. P. Priyadarshana   +3 more
doaj   +2 more sources

Integrating chemistry knowledge in large language models via prompt engineering [PDF]

open access: yesSynthetic and Systems Biotechnology
This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains.
Hongxuan Liu   +3 more
doaj   +2 more sources

Prompt engineering in higher education: a systematic review to help inform curricula

open access: yesInternational Journal of Educational Technology in Higher Education
This paper presents a systematic review of the role of prompt engineering during interactions with Generative Artificial Intelligence (GenAI) in Higher Education (HE) to discover potential methods of improving educational outcomes.
Daniel Lee, Edward Palmer
doaj   +2 more sources

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