pISSN 2288-8063

Sci Ed 2024;11(1):84-90

eISSN 2288-7474

https://doi.org/10.6087/kcse.332

Training Material

ChatGPT for editors: enhancing efficiency
and effectiveness
Yunhee Whang
Compecs Inc, Seoul, Korea

Abstract
This tutorial examines how ChatGPT can assist journal editors in improving the efficiency andeffectiveness of academic publishing. It highlights ChatGPT’s key characteristics, focusing on
the use of “Custom instructions” to generate tailored responses and plugin integration for accessing up-to-date information. The tutorial presents practical advice and illustrative examples
to demonstrate how editors can adeptly employ these features to improve their work practices.
It covers the intricacies of developing advanced prompts and the application of zero-shot andfew-shot prompting techniques across a range of editorial tasks, including literature reviews,
training novice reviewers, and improving language quality. Furthermore, the tutorial addresses
potential challenges inherent in using ChatGPT, which include a lack of precision and sensitivity to cultural nuances, the presence of biases, and a limited vocabulary in specialized fields,
among others. The tutorial concludes by advocating for an integrated approach, combining
ChatGPT’s technological advancements with the critical insight of human editors. This approach emphasizes that ChatGPT should be recognized not as a replacement for human judgment and expertise in editorial processes, but as a tool that plays a supportive and complementary role.
Keywords
ChatGPT; Prompting techniques; Journal editors; Editorial processes; Features of ChatGPT

Introduction
Received: February 2, 2024
Accepted: February 9, 2024
Correspondence to Yunhee Whangyunhee@compecs.com
ORCID
Yunhee Whang
https://orcid.org/0000-0002-0954-9468

ChatGPT (OpenAI) is a large language model that uses the generative pre-trained transformer(GPT) architecture. Since its introduction in November 2022, ChatGPT has been applied in avariety of fields. Editors and researchers have been exploring how language models like ChatGPT can effectively be utilized in academic publishing and editing to enhance both efficiency
and effectiveness [1–4]. This tutorial is designed to assist journal editors in harnessing the fullpotential of ChatGPT. It covers the core functionalities of ChatGPT, emphasizing the art ofcrafting effective prompts. The tutorial explores various prompt formulation techniques that
could generate optimal responses and provides insights into the model’s capabilities and limita-

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ChatGPT for editors

tions. It is important to acknowledge that there exists no absolute correct or incorrect way to interact with ChatGPT. Nevertheless, the adoption of specific strategies can significantly
improve the efficacy of one’s prompts. This tutorial endeavors
to acquaint editors with these methods, enabling them to employ ChatGPT more effectively in their editorial duties.

Key Features of ChatGPT
Use “Custom instructions”
By selecting the username at the bottom left of the interface,editors will find the “Custom instructions” option, situatedbeneath the “My Plan” and “My GPTs” menus. “Custom instructions” allow editors to add “preferences” or requirementsthat they would like ChatGPT to consider when generating its
responses [5,6]. This feature enables ChatGPT to tailor its responses more effectively to their needs. To activate “Custom
instructions,” two key questions must be addressed: “Whatwould you like ChatGPT to know about you to provide better
responses?” and “How would you like ChatGPT to respond?”(Fig. 1).
In response to the first question, “What would you like Chat-

GPT to know about you to provide better responses?”, editors
have the opportunity to share personal details. This may include their profession, specific goals, interests, or particularchallenges they are facing. Alternatively, editors can assign arole they envisage for ChatGPT, or a combination of both personal and role-based information. This input is crucial for themodel to customize its responses to suit the editor’s uniquecontext and informational needs. For instance, a newly appointed journal editor has a comprehensive understanding of general editorial processes. In such a scenario, it would be beneficial to identify themselves as an “experienced journal editor.”
This would prevent ChatGPT from providing basic editorial
explanations that are already familiar to the editor, thereby increasing the relevance of the guidance it offers. Similarly, if theeditor is proficient in using R, indicating this can help tailor
the depth and nature of technical explanations in the responses they receive.
For the second question, “How would you like ChatGPT to
respond?”, it is beneficial to provide details including the preferred tone (e.g., formal or conversational), desired response
length (e.g., detailed explanations or brief summaries), andspecific requests for actionable advice or suggestions. “Custom
instructions” are often overlooked by users, but they can significantly enhance both the efficiency and effectiveness of thecontent generated. Additionally, they have a significant impact on the style and appropriateness of the language used in
the response.
To fully harness ChatGPT’s capabilities, “Custom instructions” need to be thoughtfully adapted to each specific context
or situation. OpenAI’s guidance on “Custom instructions”
for ChatGPT highlights that each response has a 1,500-character limit [5]. In the absence of precise “Custom instructions,”
ChatGPT tends to deliver more generalized information, which
can lead to inconsistencies in output quality. Fig. 2 illustrates

A

B

Fig. 1. ChatGPT (OpenAI) sample responses to the questions included in “Custom instructions.”

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Fig. 2. ChatGPT (OpenAI) example responses. (A) Without “Custom instructions.” (B) With “Custom instructions.”

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Yunhee Whang•B e specific and direct: Clearly state what you want the
text to achieve. The more detailed you are in the input, thebetter the output tends to be. For example, “Write a concise summary of the latest research on X, suitable for a review article.”•D efine the style and tone: Although the desired style andtone might have been indicated in the “Custom instructions,” providing more detailed style and tone is helpful.
For instance, “Compose a persuasive argument on the importance of X, using an optimistic and engaging tone.”• S et structure (or format) expectations: If you have a particular structure (or format) in mind, such as a paragraphor bullet points, specify this in the prompt. You might say,“Draft a structured report on X, including an introduction,
three key points, and a conclusion.”•R equest examples and evidence: Ask for the inclusion of
examples, data, or quotations to improve clarity and authority. For example, “Explain the concept of herd immunity and support the explanation with real-world examples
and statistical evidence.”• I ndicate the audience: Knowing the audience helps tailor
the language complexity and terminology. For instance,“Explain X for experienced journal editors.”•L imit word count: If brevity is important, set a word limit. For example, “In no more than 100 words, summarize
the key findings of the recent study on X.”•A sk for a specific perspective or angle: This can guidethe content’s direction. For example, “From the perspectiveof a cardiovascular surgeon, discuss X.”

the difference in responses with and without the use of “Custom instructions.”
Use plugins
Enhancing ChatGPT with plugins markedly expands its functionality and usefulness, especially for researchers and journal
editors. ChatGPT’s base knowledge is limited to data up to
April 2023. However, integrating plugins enables access to current, real-time information, surpassing the inherent limitations of ChatGPT’s dataset. Popular plugins in the academic
community include Scholar AI, PubMed, Consensus Search,
and AskyourPDF among others. Notably, a maximum of
three plugins can be activated simultaneously.
The full potential of these plugins is realized when they areintegrated with “Custom instructions.” This tailored approachensures that the results are precisely aligned with the specificcontext and requirements of the user. For example, designating a “researcher” or “journal editor” role in the “Custom instructions” significantly influences the nature of the output. In
leveraging these role-specific cues, ChatGPT, in conjunction
with the activated plugins, provides information that is notjust up to date, but also highly relevant to the distinct needs ofresearchers and journal editors. This strategic customization
helps users receive information that is both current and directly applicable to their specific professional needs.

Write Effective Prompts
Guidelines
Crafting a precise question or command is essential to obtaining the desired response from ChatGPT. While there is considerable flexibility in how one may interact with AI, following specific guidelines can substantially improve the accuracy
and effectiveness of the prompts. This section outlines key
principles (adapted from ChatGPT) and examples for generating prompts.

Types of prompting techniques
In this section, we explore two pivotal prompting techniquesfor AI language models: zero-shot and few-shot prompting[7,8]. These methods are crucial for editors looking to optimize their prompt crafting skills for effective interactions with
AI systems like ChatGPT. Table 1 presents a comparison high-Table 1. Comparative analysis of zero-shot and few-shot prompting techniques in ChatGPT
Category

Zero-shot prompting

Few-shot prompting

Reliance on pre-trained knowledge

High

Moderate

Need for specific training data

None

Some examples needed

Application in various tasks

GeneralSpecific tasks where examples are provided

Strength

Quick, unbiased assessments

Setting standards or examples in unfamiliar or variable situations

Limitation

Less tailored to specific tasks

Risk of overfitting to examples

Accuracy

Variable, based on pre-trained data

Can be higher with relevant examples

Data sensitivity

Standard privacy risks

Additional risks with example data

Customization potential

LimitedHigh, through tailored examples

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ChatGPT for editors

lighting the distinct characteristics of each technique.
This comparative analysis is designed to assist editors in
discerning the most suitable prompting approach for varying
editorial contexts. These techniques can significantly enhancethe efficiency and effectiveness of editorial processes in academic publishing. Specific examples of how these techniques
can be applied are presented in the following section, which
discusses the use of ChatGPT in the editorial process.

How can Editorial Processes Benefit from ChatGPT?
The integration of ChatGPT into the editorial workflow offers
significant benefits. First, it can be used to conduct preliminary literature reviews, identifying key themes, methodologies, and gaps in the literature. Fig. 3 illustrates this process,utilizing the zero-shot prompting technique and incorporating plugins like Scholar AI. These plugins are particularly valu-

Fig. 3. ChatGPT (OpenAI) sample results using plugins (PubMed Research, Consensus Search, Scholar AI) and the zero-shot prompting technique.

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Yunhee Whang

able as they access recent information, supplementing ChatGPT’s knowledge base, which is current until April 2023. Editors can obtain better results by combining “Custom instructions,” proper prompting techniques, and plugins.
Second, ChatGPT can serve as a training tool for novice editors and reviewers, guiding them to provide constructive,
clear, and professional feedback. Consider a scenario where ajournal editor aims to improve the quality of peer review comments from inexperienced reviewers. The editor could employ
the few-shot prompting technique with ChatGPT. Initially, theeditor selects two to three exemplary peer reviews, showcasing
attributes like constructive feedback, clarity, thoroughness, andprofessionalism. The novice reviewers then use these models
to train ChatGPT, alongside specific instructions from the editor. For instance, they might say, “Here are three high-quality
peer reviews, each exemplary in certain aspects. Use these as
benchmarks to refine my draft comments.” The novice reviewers input their initial comments, which might be unrefined or overly critical, into ChatGPT. Guided by the model
reviews, ChatGPT then revises the comments to align withthe demonstrated standards, improving their structure, language, and tone while preserving the core intent and criticalobservations. This method streamlines and elevates the editorial process.
Third, ChatGPT provides valuable assistance in language
editing. This is especially beneficial for non-native Englishspeaking editors, helping them ensure clarity and coherencein their editorial work. It effectively bridges communication
gaps within academic discourse, maintaining the standards ofinternational academic publications. Below are examples ofsimple editing-related prompts: “Clarify the text below andcorrect any spelling, grammar and punctuation errors if there
are any,” “Improve flow, academic tone, and overall quality,”and “Improve language style, flow, and readability.” If editors
want to double-check the changes, they may add an additional prompt—”Bold all changes you make.” It is crucial to recognize that enabling “Custom instructions” significantly influences each output. For instance, Fig. 4A displays a basic editing response from ChatGPT without “Custom instructions”
activated. Fig. 4B and 4C, using the same prompt, “Proofread
the following draft,” demonstrates how “Custom instructions”
modify ChatGPT’s responses. Notably, Fig. 4C shows thatwhen “Custom instructions” request explanations, ChatGPT
provides detailed rationales for each edit. This highlights thefact that effective prompt crafting, while important, is not thesole factor in optimizing results. The incorporation of welldesigned “Custom instructions” significantly increases ChatGPT’s utility, producing results that are more closely alignedwith the specific requirements of editors.
Some other example prompts that can be effectively utilized

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A

B

C

Fig. 4. ChatGPT (OpenAI) example responses. (A) Without “Custom instructions” enabled. (B) With “Custom instructions” enabled. (C) With explanations
requested in “Custom instructions.”in the editorial process are as follows:• Does this research paper on X adhere to the standard ethical guidelines for scientific writing?
• What are the current trends in X, and how do they compare to trends from 5 years ago?
• What are the best practices for conducting a blind peer
review for a scientific journal?
• How might developments in X influence future researchin Y?
• What are the ethical considerations when publishing research involving human subjects?

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ChatGPT for editors• What are the guidelines for managing conflicts of interestin academic publishing?

Challenges and Limitations of Using ChatGPT
Integrating ChatGPT into the editorial process, particularly
for non-native English-speaking editors, presents unique challenges. One significant challenge lies in the tool’s inherent dependency on its training data, which can be a source of biases
and inaccuracies in its responses. This limitation is especiallyproblematic for scientific writing, which requires a high degree of precision and accuracy. In addition, ChatGPT may notalways capture the dynamic and culturally diverse nuances ofacademic writing. Its responses may miss the subtleties andcontext-specific intricacies that are vital in scholarly communication. For example, in medical research, cultural factors
can significantly influence both the research itself and its interpretation, and an incomplete understanding of these issues
can lead to oversimplified or even inaccurate representationsof the subject matter. This limitation heightens the risk of misunderstanding or misinterpretations in editing, with the potential to significantly compromise the authenticity and integrity of academic work.
Furthermore, ChatGPT can sometimes produce responses
with varying degrees of consistency, particularly when dealing
with long or complex texts that require a uniform style andspecialized vocabulary. In academic writing, maintaining aconsistent style and tone is vital for the clarity and readabilityof scientific papers. Additionally, ChatGPT may not always
correctly deploy the specific technical vocabulary or jargon
used in certain scientific fields. This limitation can lead to a
gap in communication, especially when dealing with highly
specialized or emerging areas of research where precise terminology is the key. Editors may also find some phrases that
tend to be overused during interactions with ChatGPT. Users
have been sharing lists of overused phrases in discussions on
platforms such as Reddit [9] and Medium [10]. When reviewing or editing content, it is essential to watch out for thesephrases and consider replacing them with more diverse andcontextually appropriate language to improve the overall
quality of the text and avoid clichés.

Conclusion
ChatGPT is a sophisticated generative language model andoffers a wide array of capabilities that extend beyond simple
text generation. The key to harnessing its full potential lies inunderstanding and effectively utilizing its main features, including “Custom instructions,” which allow assigning rolesand control the output. Rather than merely replicating prompts

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used by others, it is crucial for editors to comprehend how
combining “Custom instructions” with thoughtfully crafted
prompts can yield customized and superior results. Although
ChatGPT demonstrates proficiency in broad language tasks,
its effectiveness in highly specific or specialized domains may
encounter constraints. Therefore, while ChatGPT can substantially assist with editorial tasks, it should be used as a complement to, rather than a replacement for, human expertise andjudgment.

Conflict of Interest
No potential conflict of interest relevant to this article was reported.

Funding
The author received no financial support for this work.

Data Availability
Data sharing is not applicable to this article as no new data
were created or analyzed.

Supplementary Materials
The author did not provide any supplementary materials forthis article.

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