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Saturday, May 06, 2023

ChatGPT Prompts Guide for Standard Undergrade Reference Questions

When creating prompts for ChatGPT, it is imperative to consider several parameters to ensure that the generated responses are relevant, accurate, and aligned with the desired outcome. Crafting a prompt that is clear, concise, and unambiguous is crucial. This can be achieved using simple and direct language and being specific about the expected outcome. Additionally, it is essential to consider the context in which the prompt will be used and tailor it accordingly. Considering these factors, a prompt that is effective and easy to comprehend can be created.

Designing prompts for an AI requires a specific and focused approach. First, sufficient context must enable the AI to generate a relevant response. The AI may understand the user's intent and respond reasonably. Therefore, it is crucial to craft well-thought-out prompts that clearly convey the user's needs and expectations. By doing so, we can ensure that the AI provides accurate and helpful assistance to users.

Phrasing is another critical aspect to consider when designing prompts for an AI. Using language that encourages the AI to provide a detailed and informative response is essential. This is in addition to other factors, such as the type of data being used and the intended audience. By carefully crafting the prompt, we can ensure that the AI provides the most accurate and helpful information possible.

By considering these parameters, the prompts generated for ChatGPT can be optimized to produce high-quality responses that meet the user's needs. The quality of the responses generated by ChatGPT can significantly impact the user's experience. 

By optimizing the prompts, we can ensure that the responses are relevant, accurate, and helpful, leading to increased user satisfaction and engagement. Additionally, optimizing the prompts can reduce the time and effort the user requires to find the information they need, further improving their experience. Overall, considering these parameters is essential to ensure that ChatGPT provides the best possible experience for its users.

The presented text outlines a table that offers examples of how each parameter can be utilized to broaden or restrict a reference question's focus.

  • By adjusting these parameters, the extent and comprehensiveness of the review can be tailored to align with specific research objectives or areas of interest. 
  • This level of customization enables researchers to conduct a more targeted and efficient literature review, ultimately saving valuable time and resources. 
  • By carefully selecting the appropriate parameters, researchers can ensure that their literature review is comprehensive enough to provide a thorough understanding of the topic and focused enough to provide valuable insights.

As a language model, ChatGPT does not inherently have specific "research prompt parameters." However, here is a table defining and providing examples of parameters you can use to create effective prompts for ChatGPT:






The degree to which a prompt is clear, concise, and easily understood by the language model.

A clear prompt has a specific objective and is phrased so ChatGPT can easily understand and generate responses.

"What are the main factors contributing to climate change?" instead of "Tell me about that climate thing."


Background information was provided to help ChatGPT understand the prompt and generate relevant responses.

Context helps establish the foundation for a prompt and may include relevant facts, figures, or timeframes.

"Considering the events of World War II, explain the significance of the D-Day invasion."

Open-ended vs. Closed-ended

The type of question posed requires a detailed response (open-ended) or a specific, limited answer (closed-ended).

Open-ended prompts encourage broader, more thoughtful responses, while closed-ended prompts seek a specific answer.

Open-ended: "Discuss the impact of social media on society." Closed-ended: "When was Facebook founded?"


The emotional quality or style of the prompt can influence the tone of ChatGPT's response.

The tone can be formal, informal, serious, light-hearted, etc., depending on the desired response style.

Formal: "Please provide a detailed analysis of Shakespeare's use of soliloquies." Informal: "What's up with soliloquies in Shakespeare's plays?"

ChatGPT Citation Style Parameters and Their Purpose

Here is a working ChatGPT prompt for generating an APA citation for a journal article:

Generate an APA citation for a journal article with the following information: author: John D. Smith, Jane A. Doe; title: The impact of AI in drug discovery; journal: Journal of Drug Discovery; publication year: 2021; volume: 7; issue: 3; page range: 145-152; DOI: 10.1111/jdd.12345

This prompt will instruct an AI model to generate an APA citation based on the provided information about the journal article as below.

Smith, J. D., & Doe, J. A. (2021). The impact of AI in drug discovery. Journal of Drug Discovery, 7(3), 145-152.

Prompt Engineering for Literature Reviews with ChatGPT

Prompt engineering is an essential practice in conducting ChatGPT literature reviews. 

Syntax and terminology are essential elements of prompt engineering as they help accurately capture relevant information from databases and other source materials during the literature review process.

Syntax refers to the structure, form, and order of words and phrases used in a language. For example, when conducting a literature review, a librarian might use the syntax “engineering AND materials” when querying an online database. This syntax helps ensure that the search returns results related to engineering and materials.

Terminology is a set of words and phrases related to a specific subject or field of study. For example, if a librarian is conducting a literature review on automotive engineering, they might use terminology such as “engine,” “transmission,” and “turbocharger.” This terminology helps to refine the query and return results that are more relevant to the literature review.

In addition to syntax and terminology, librarians should be familiar with the syntax and terminology specific to the database they use. Different databases have different ways of searching and retrieving information. If a librarian is not familiar with the specific syntax and terminology used by a database, they could end up missing relevant information that could be found using the correct syntax.

Finally, librarians should understand the logic of using syntax and terminology when conducting literature reviews. This knowledge helps them construct more efficient, accurate queries that narrow the search to only relevant information. Understanding how to properly use syntax and terminology for literature reviews is essential for research librarians.

Identifying Research Gaps with ChatGPT Prompts

Identifying Research Gaps with ChatGPT Prompts

As academic researchers, it is essential to understand the importance of crafting effective prompts for chatbots. These prompts serve as a means of engaging with users and providing them with valuable information on various topics. However, to successfully create these prompts, we must understand the syntax and terminology necessary for proper implementation.

One way to achieve this level of comprehension is by conducting thorough research into existing literature related to our field or subject matter. By analyzing trends and identifying gaps in knowledge, we can formulate relevant research inquiries that will help us develop more targeted chatbot prompts.

As librarians who teach researchers how best to conduct their work effectively, you play a crucial role in facilitating access to resources that support such endeavors. Therefore I encourage you to stay up-to-date on emerging technologies like Chatbots so that your patrons have all they need when embarking upon new projects or exploring uncharted territories within academia.

Example ChatGPT Prompt to Identify Research Gaps 

lit_review: topic: artificial intelligence in drug discovery; subtopics: molecular docking, target prediction, drug repurposing; time_period: 2015-2021; relationship: AI techniques, drug discovery stages; trend_analysis: AI advancements, adoption in pharmaceutical industry; methodologies: computational models, validation methods; research_questions: future AI integration, ethical considerations





Indicates the intent to perform a literature review for identifying gaps or areas requiring further study.

"lit_review: What are the gaps in knowledge related to climate change and public health?"


Specifies the main topic or subject of the literature review.

"lit_review: topic: machine learning in healthcare."


Lists the subtopics or areas within the main topic that should be examined.

"lit_review: topic: AI in education; subtopics: personalized learning, assessment, teacher support."


Determines the range of publication years for the literature review.

"lit_review: topic: renewable energy; time_period: 2010-2021"


Asks for an analysis of the relationships between specified topics or trends.

"lit_review: topic: social media and mental health; relationship: usage, addiction, well-being"


Requests an evaluation of the evolution or changes in the selected topic(s) over time.

"lit_review: topic: blockchain technology; trend_analysis: development, adoption, applications"


Queries about the methodologies used in the existing literature and if there are gaps or limitations.

"lit_review: topic: cancer immunotherapy; methodologies: experimental design, data analysis, reproducibility."


Instructs the model to generate research questions based on the identified gaps or areas requiring investigation.

"lit_review: topic: microplastics in the environment; research_questions: sources, impacts, mitigation strategies"

ChatGPT4 Results

Title: A Literature Review on Artificial Intelligence in Drug Discovery (2015-2021): A Focus on Molecular Docking, Target Prediction, and Drug Repurposing


This literature review explores the role of artificial intelligence (AI) in drug discovery, focusing on molecular docking, target prediction, and drug repurposing from 2015 to 2021. The review analyzes the relationship between AI techniques and drug discovery stages, trends in AI advancements and their adoption in the pharmaceutical industry, and the methodologies employed in computational models and validation methods. Additionally, this review raises research questions regarding the future integration of AI in drug discovery and the ethical considerations of such advancements.

1. Introduction

The application of AI in drug discovery has grown significantly over the past decade, with promising advancements in molecular docking, target prediction, and drug repurposing. This literature review aims to provide a comprehensive overview of these subtopics and the methodologies employed in computational models and validation methods. Furthermore, the review identifies trends in AI advancements and their adoption in the pharmaceutical industry and discusses potential future AI integration and ethical considerations.

2. AI in Molecular Docking

Molecular docking is a vital step in drug discovery, aiming to predict the preferred orientation of a molecule when bound to a protein target. The application of AI techniques in molecular docking has led to enhanced accuracy, efficiency and the discovery of novel compounds. This section reviews the most recent AI-based molecular docking techniques, including deep learning, reinforcement learning, and swarm intelligence algorithms.

3. AI in Target Prediction

Target prediction involves identifying the molecular targets of bioactive compounds, which is crucial for understanding the mechanism of action and potential side effects of drugs. AI techniques, including machine learning and neural networks, have significantly improved target prediction accuracy, specificity, and sensitivity. This section covers the critical AI-driven advancements in target prediction, including multi-target prediction, chemical similarity-based methods, and data mining techniques.

4. AI in Drug Repurposing

Drug repurposing aims to find new uses for existing drugs, thus saving time and resources compared to de novo drug discovery. AI has enabled the rapid identification of drug-repurposing candidates by analyzing large-scale data sets and predicting drug-target interactions. This section discusses the most notable AI-powered drug repurposing techniques, such as machine learning algorithms, network-based approaches, and natural language processing methods.

5. Methodologies: Computational Models and Validation Methods

This section analyzes the computational models and validation methods employed in AI-based drug discovery research. We focus on data preprocessing, feature selection, model training, and validation in developing accurate and robust AI models. Additionally, we discuss the challenges and limitations associated with these methodologies.

6. Trend Analysis: AI Advancements and Adoption in the Pharmaceutical Industry

The growing interest in AI-driven drug discovery has led to increased collaboration between academia, biotech companies, and pharmaceutical firms. This section analyzes the trends in AI advancements and their adoption in the pharmaceutical industry. In addition, it highlights the factors contributing to this growth and the challenges faced in integrating AI into drug discovery.

7. Research Questions and Ethical Considerations

As AI revolutionizes drug discovery, several research questions and ethical considerations arise. This section explores potential future AI integration in drug discovery, addressing the potential risks and benefits, the role of AI in personalized medicine, and the need for transparency and accountability in AI-driven research. Additionally, we discuss the ethical implications of AI use in drug discovery, including data privacy, biases, and the potential impact on the global healthcare landscape.

8. Conclusion

This literature review provides a comprehensive overview of AI applications in drug discovery, focusing on molecular docking, target prediction, and drug repurposing. Through trend analysis and a discussion of methodologies, it offers insights into the growing role of


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