Justin B. White

About

Justin B. White, MD, is an Associate Professor at Virginia Tech Carilion School of Medicine. His role involves contributing to medical education and research within the institution. He is affiliated with the Virginia Tech Carilion School of Medicine and is involved in the academic and clinical activities of the institution, supporting the development of future healthcare professionals.

Research topics

• Computer Science
• Programming language
• Artificial Intelligence
• Computer Security
• Software engineering
• Engineering
• Data science
• Operating system

Selected publications

• A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

  arXiv (Cornell University) · 2023 · 786 citations

  1st authorCorresponding

  Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM. This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied…

  PublisherOA PDFDOI

• The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

  arXiv (Cornell University) · 2024-06-06 · 80 citations

  preprintOpen access

  Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains. Developers and end-users interact with these systems through the use of prompting and prompt engineering. Although prompt engineering is a widely adopted and extensively researched area, it suffers from conflicting terminology and a fragmented ontological understanding of what constitutes an effective prompt due to its relatively recent emergence. We establish a stru…

  PublisherOA PDFDOI

• ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

  arXiv (Cornell University) · 2023-03-11 · 39 citations

  preprintOpen access1st authorCorresponding

  This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and simulating a web application API before it is implemented. This paper provides two contributions to research on using LLMs for software engineering. First, it provides a catalog of patterns for software e…

  PublisherOA PDFDOI

• Evaluating Persona Prompting for Question Answering Tasks

  2024-06-22 · 16 citations

  articleOpen accessSenior author

  Using large language models (LLMs) effectively by applying prompt engineering is a timely research topic due to the advent of highly performant LLMs, such as ChatGPT-4. Various patterns of prompting have proven effective, including chain-of-thought, self-consistency, and personas. This paper makes two contributions to research on prompting patterns. First, we measure the effect of single- and multi-agent personas in various knowledge-testing, multiple choice, and short answer environments, using…

  PublisherDOI

• Semantic Compression With Large Language Models

  arXiv (Cornell University) · 2023-04-25 · 4 citations

  preprintOpen accessSenior author

  The rise of large language models (LLMs) is revolutionizing information retrieval, question answering, summarization, and code generation tasks. However, in addition to confidently presenting factually inaccurate information at times (known as "hallucinations"), LLMs are also inherently limited by the number of input and output tokens that can be processed at once, making them potentially less effective on tasks that require processing a large set or continuous stream of information. A common ap…

  PublisherOA PDFDOI

Frequent coauthors

• Douglas C. Schmidt

  Vanderbilt University

  116 shared

• Michael Sandborn

  92 shared

• Carlos Olea

  89 shared

• Sam Hays

  88 shared

• Mohamed W. Hassan

  King Abdullah University of Science and Technology

  81 shared

• F Omer

  University of Messina

  81 shared

• Claudio A. Ardagna

  University of Milan

  81 shared

• Albert Carlson

  Austin Community College

  81 shared

Labs

• Health Systems and Implementation SciencePI

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• Michael J. Friedlanderh-42
• Robert L. Trestmanh-42