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

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

Syntax/Term	Description	Example
lit_review	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?"
topic	Specifies the main topic or subject of the literature review.	"lit_review: topic: machine learning in healthcare."
subtopics	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."
time_period	Determines the range of publication years for the literature review.	"lit_review: topic: renewable energy; time_period: 2010-2021"
relationship	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"
trend_analysis	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"
methodologies	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."
research_questions	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

Abstract:

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

Navigating Pitfalls in ChatGPT Research Prompts - A Guide for Librarians

This table provides examples of bad and good prompts for each of the mentioned pitfalls. You can create well-structured, effective, and meaningful research prompts that contribute to high-quality, impactful research by avoiding these issues.

Pitfall	Bad Prompt Example	Good Prompt Example
Ignoring ethical considerations	What are the most effective torture techniques law enforcement uses for extracting information?	How can law enforcement balance suspect interrogation with respecting human rights and ethical considerations?
Creating unfeasible research	Analyze the impact of every book published in the last century on modern literature.	What are the key themes and trends in modern literature influenced by seminal works published in the last century?
Lack of originality	What factors contribute to climate change?	How do socioeconomic factors influence climate change adaptation and mitigation strategies at the local level?
Overemphasis on personal opinion	Why is my favorite book the best work of literature ever written?	What literary elements and themes contribute to the lasting popularity of classic novels?
Overcomplicating the prompt	Analyze the political, economic, social, technological, legal, and environmental factors affecting the sales of electric cars in the last five years in 20 countries.	What are the key factors affecting the adoption of electric vehicles in major markets over the past five years?
Inadequate context	How did that historical event impact society?	How did the Civil Rights Movement in the United States impact racial equality and social reform?
Ignoring interdisciplinary opportunities	How has climate change impacted agriculture solely from an economic perspective?	How has climate change impacted agriculture, considering economic, environmental, and social perspectives?
Focusing solely on a single methodology	Conduct a quantitative analysis of the psychological effects of social media on teenagers.	Investigate the psychological effects of social media on teenagers using quantitative and qualitative approaches.
Neglecting the target audience	What are the legal implications of nanotechnology advancements in medical treatments? (For a general audience)	How do advancements in nanotechnology impact the medical field and its treatments in layman's terms?
Overlooking the importance of a clear objective	What do people think about social media?	How do different age groups perceive the role of social media in their lives, and what factors influence their opinions?
Assuming prior knowledge	How does the activation of the RAS-ERK pathway affect cellular functions? (For a non-specialist audience)	How does activating a specific cellular signaling pathway influence cell functions, explained in accessible terms?
Relying on outdated or biased sources	How do outdated gender roles contribute to a successful marriage?	How have evolving gender roles impacted the dynamics of modern marriages and partnerships?

As a reference librarian, your role in guiding researchers in crafting research prompts is vital. To create compelling and meaningful research prompts, it is essential to consider the following guidelines:

Avoid discriminatory language or leading questions

Ensure the prompts contain no language that may discriminate against a particular group or influence the participant's response. This will help maintain the integrity of the research process and facilitate unbiased, accurate data collection.

Use clear concise language

Employ language that accurately conveys the intended meaning and is easily understandable by the target audience. This will help avoid misunderstandings and ensure the research remains focused and productive.

Balance breadth and specificity

Aim to craft research prompts that are broad enough and narrow enough. Consider the scope of the research question and available resources when creating a manageable and informative prompt. Striking the right balance will help researchers maintain a focused and comprehensive investigation.

Avoid leading questions

Refrain from using questions that imply a particular answer or outcome, as they can introduce bias into the research process. Instead, use neutral questions that allow for a more thorough analysis, leading to reliable and valid conclusions.

Steer clear of double-barreled questions

Focus on one question or topic at a time to ensure clarity and coherence in the research prompts. Addressing multiple questions simultaneously can be confusing and lead to imprecise or conflicting findings.

Minimize jargon and complex language

Keep the language of the research prompts simple. Excessive jargon or complex language can hinder comprehension and create barriers for researchers. The clear and accessible language will ensure that the research question is easily understood and approachable.

By adhering to these guidelines, you will be better equipped to assist patrons in developing well-crafted research prompts that lead to high-quality, impactful research. In addition, your guidance will be invaluable in helping researchers navigate the complexities of the research process, ensuring their work is focused, efficient, and effective.