Advanced Techniques for Research with ChatGPT

Research has always been essential to human progress and has evolved tremendously over the past few years. With the advent of advanced technologies, new tools and techniques have emerged to conduct more efficient research and to stay at the forefront of knowledge. One such technology is ChatGPT, a large language model that uses deep learning approaches to generate human-like responses. The remarkable ability of ChatGPT to understand and develop text has made it an invaluable tool that can enhance your research. Your productivity as a researcher is improved as it saves time and resources by providing comprehensive insights. However, researchers must be careful about the ethical considerations of using ChatGPT and ensure that their research is accurate and unbiased.

In this post, you will explore the advanced techniques to improve your research. In particular,

  • Analyzing and Interpreting Research Data
  • Performing Literature Review & Identifying Research Gaps

Get started and apply ChatGPT with my book Maximizing Productivity with ChatGPT. It provides real-world use cases and prompt examples designed to get you using ChatGPT quickly.

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Advanced Techniques for Research with ChatGPT.
Picture generated by Adrian Tam using Stable Diffusion. Some rights reserved.


This post is divided into three parts; they are:

  • Analyzing and Interpreting Research Data
  • Performing Literature Review & Identifying Research Gaps
  • Bonus Prompts for Researchers

Analyzing and Interpreting Research Data

A small business owner wants to analyze customer satisfaction data to improve customer service. They collected this data from 50 customers using the Likert Scale Questionnaire with 10 questions. The data is numerical ratings from 1 to 5, where 1 indicates “Very Dissatisfied” while 5 indicates “Very Satisfied”. Interpreting this raw data using conventional methods will be time-consuming and requires dedicated software expertise. This is where you can use ChatGPT to help you make sense of the data.

You will see how we will ask questions to assist this business owner. Before proceeding toward the prompts, here is the snapshot of the data that we will be working on:

Customer ID Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10
1 4 5 3 5 4 3 4 4 5 3
2 3 4 2 4 3 2 3 3 4 2
3 5 5 4 5 5 5 4 5 5 4
4 2 3 2 4 2 2 3 3 2 2
5 4 4 3 4 4 4 4 4 4 3
6 3 4 2 4 3 3 3 3 4 2
7 1 2 1 2 1 1 2 2 1 1
. . . . . . . . . . .
. . . . . . . . . . .
49 2 2 1 2 1 1 2 2 1 1
50 3 4 2 4 3 3 3 3 4 2

Step 1: Data Analysis & Exploration

Data exploration is an essential step in data analysis that helps you understand the data and identify the key insights. Before prompting the ChatGPT, enlist the specific questions you want to be answered. Incorporating these questions will guide the data exploration part. Here is the final prompt that the business owner provides to the ChatGPT:

I would like you to help me with the initial data exploration of the customer satisfaction ratings dataset that I collected. Please provide insights on the following:
1) Can you provide summary statistics of the customer satisfaction rating dataset, including the range, mean, median, and mode of the ratings?
2) How many customers gave the highest rating (5) for each question, and how many gave the lowest rating (1)?

Dataset: {}

The following is the response from ChatGPT:

To perform the initial data exploration of the customer satisfaction rating dataset, let’s start by calculating the summary statistics, including the range, mean, median, and mode of the ratings.

Here are the summary statistics for each question in the dataset:

Question 1 (Q1):
Range: 4 (5 – 1)
Mean: 3.08
Median: 3
Mode: 4 (appears most frequently) … so on upto Question 10 (Q10)

Here are the summary statistics for the customer satisfaction rating dataset you provided:

Range: The range of ratings is from 1 to 5.
Mean: The mean rating across all customers and questions is 3.36.
Median: The median rating is 4.0, which means that half of the ratings are below 4 and a half are above.
Mode: The mode represents the most common rating in the dataset. In this case, there are multiple modes, with a rating of 4 being the most frequent rating.

Now, let’s find out how many customers gave the highest rating (5) and the lowest rating (1) for each question:

For Question 1 (Q1):
Number of customers who gave a rating of 5: 5
Number of customers who gave a rating of 1: 3 … so on upto Question 10 (Q10)

Please note that the above counts are based on the dataset you provided. If you have more data, the counts may differ.

Step 2: Interpreting Key Insights

Now, that you have a general overview of the data, let us draw the key insights the company can leverage and the common reasons for customer complaints. Considering the abovementioned things, the business owner crafts a new prompt and feeds it to ChatGPT. The follow-up prompt after step 1 is as follows:

Based on the above statistics, what are the key insights I can draw from the data? Can you also provide information about the following:
1. Key drivers of customer loyalty based on available data?
2. Common reasons for customer complaints or negative feedback?

Since we have considered dummy data where the questions are just like the black box, ChatGPT cannot provide us recommendations about how to improve our strategy but will pinpoint the areas of improvement.

Here is the response from ChatGPT:

Based on the provided statistics, we can draw the following key insights from the data:

Average Rating: The average rating across all customers and questions is 3.36, which is slightly below the midpoint of 5. This suggests that overall customer satisfaction is moderate.

Positive Ratings: The most frequent rating is 4, which indicates that many customers are generally satisfied with the service or product.

Negative Ratings: There are a few customers who have given the lowest rating of 1 for some questions. This indicates that there are areas where improvement is needed to address customer concerns.

Question 10 (Q10): This question received the highest number of lowest ratings (5 customers gave a rating of 1). It may be worth investigating the specific issues raised in this question to identify potential areas for improvement.

Now, let’s analyze the data to identify the key drivers of customer loyalty based on the available information. To determine this, we can look at the questions that received the highest number of 5 ratings:

Question 2 (Q2): This question received a rating of 5 from all customers. It suggests that the aspect addressed in this question is a key driver of customer loyalty. Unfortunately, without the actual question content, it’s difficult to determine the specific driver.

Questions 1, 4, 5, 6, 7, 8, and 9: These questions also received a rating of 5 from all customers. Similar to Q2, the specific drivers of loyalty cannot be identified without knowing the content of these questions.

Regarding common reasons for customer complaints or negative feedback, the data indicates that Question 10 (Q10) received the highest number of lowest ratings (5 customers gave a rating of 1). It suggests that there are specific issues addressed in Q10 that lead to customer dissatisfaction. To identify the common reasons for negative feedback, it’s essential to review the content of Question 10 and analyze the responses provided by the customers. Without that information, we cannot provide specific insights into the common complaints.

To gain deeper insights and a more comprehensive understanding of the data, it is recommended to review the actual questions and analyze the qualitative feedback provided by customers, if available. This will provide more context and enable more accurate identification of key drivers of loyalty and common reasons for complaints.

You have seen how helpful ChatGPT can be to business owners so they can make data-driven decisions. However, one crucial thing that the researchers should be mindful of is to avoid biased language while framing the prompts or input data. This can lead to biased results. Using high-quality training data representing the whole population is also helpful in ensuring the research is accurate and unbiased.

Performing Literature Review & Identifying Research Gaps

Zeenat is a post-graduate Researcher. She aims to perform a literature review on renewable energy resources and their integration into the power grid. She wants to identify the potential gaps in this area that can help her formulate the research questions and help her in the literature review process. You will now witness the 1-1 conversation between Zeenat and ChatGPT:

[Zeenat]: I need help with a literature review on renewable energy sources and their integration into the power grid. Can you provide me with an overview of the current state of research in this area?

[ChatGPT]: Certainly! The integration of renewable energy sources into the power grid has been a topic of extensive research in recent years. Here’s an overview of the current state of research in this area:

1. Renewable Energy Technologies: Numerous renewable energy technologies are being studied and deployed, including solar photovoltaic (PV) systems, wind turbines, hydroelectric power, biomass, geothermal, and tidal energy. Researchers are investigating the technical aspects, efficiency, and scalability of these technologies.
2. Grid Integration Challenges: One major focus of research is understanding and addressing the challenges associated with integrating renewable energy into the power grid. These challenges include intermittency and variability of renewable generation, grid stability, voltage control, frequency regulation, and power quality issues.
…. so on…

ChatGPT has a knowledge cutoff of September 2021, so ChatGPT might not be aware of the recent events. It provides a comprehensive overview of the current challenges and work done so far, but the response should also be verified from other sources. Let’s get back to Zeenat’s conversation with ChatGPT:

[Zeenat]: Can you provide a list of the top 10 most-cited papers on renewable energy integration into the power grid?

[ChatGPT]: I can give you a general idea of some influential papers in the field that were highly cited as of my knowledge cutoff date. These papers have contributed significantly to the understanding and development of renewable energy integration into the power grid:

1. “Power System Stability and Control” by Prabha Kundur
2. “Large-scale Renewable Power Generation: Integration and Sustainability” by Peter Droege
3. “Power System Analysis” by Hadi Saadat
4. “Renewable Energy Sources and Climate Change Mitigation” by Ottmar Edenhofer et al.
5. “Renewable Energy Integration: Practical Management of Variability, Uncertainty, and Flexibility in Power Grids” by Lawrence E. Jones
6. “The Smart Grid: Enabling Energy Efficiency and Demand Response” by Clark W. Gellings
7. “Renewable Energy Grid Integration: Technical and Regulatory Issues” by Lawrence E. Jones
8. “Renewable and Efficient Electric Power Systems” by Gilbert M. Masters
9. “Renewable Energy: Power for a Sustainable Future” by Godfrey Boyle
10. “Renewable Energy Integration: Challenges and Solutions” by Fereidoon P. Sioshansi

Please note that this list is not exhaustive and there may be other highly cited papers in the field that are not included here. I recommend consulting academic databases, such as Google Scholar or Scopus, to find the most up-to-date and comprehensive list of highly cited papers on renewable energy integration into the power grid.

Collecting the most cited and influential papers is time-consuming, mainly when the information is scattered across various resources. However, you can save much time by using ChatGPT as your research assistant. Now that you have the list of the top 10 cited papers, Zeenat needs to identify the potential research gaps that can form the basis for her thesis.

[Zeenat]: Based on the current research, what are the main challenges and research gaps that need to be addressed in the integration of renewable energy sources into the power grid?

[ChatGPT]: The integration of renewable energy sources into the power grid poses several challenges and research gaps that are being actively addressed by researchers and industry experts. Here are some of the main challenges and research areas:

1. Grid Stability: The intermittent nature of renewable energy sources, such as solar and wind, can impact grid stability. Developing advanced control and forecasting techniques can help address this issue.

2. Energy Storage: Efficient and cost-effective energy storage solutions are needed to store excess renewable energy for use during periods of low generation.

3. Grid Management: As renewable energy sources become more prevalent, there is a need for better grid management techniques, such as demand response and distributed energy resource management.

4. Regulatory Frameworks: Addressing the regulatory challenges related to renewable energy integration, such as grid access, pricing, and incentives, remains an important research area.

5. Technological Innovations: Continued research on new technologies and materials that can improve the efficiency and cost-effectiveness of renewable energy generation and storage is crucial.

6. System Integration: Research on the integration of renewable energy sources with existing power infrastructure, including the interplay between different energy sources and grid components, is needed.

These are some of the key challenges and research gaps in the integration of renewable energy sources into the power grid. Researchers, policymakers, and industry stakeholders are actively working to address these challenges and develop innovative solutions to accelerate the transition to a sustainable and renewable energy-based grid

With ChatGPT’s help, Zeenat could understand the current state of the research and identify the potential gaps that helped her develop the research questions for her thesis. But you need to be aware of important considerations other than the knowledge limitations; these are as follows:

  • Accuracy and Reliability: ChatGPT is a language model, so the responses cannot be declared 100% accurate. You need to cross-verify your answers and consult additional sources.
  • Ethics and Bias: Researchers should strive to maintain ethical research standards and be aware of potential biases in ChatGPT’s responses.

Bonus Prompts for Researchers

This bonus section contains the prompts that researchers can use to make their research process more manageable. From literature reviews to data collection to writing up the results, you can streamline your research workflow using these prompts:

Generating Topic Ideas

“Can you suggest 5 important unanswered questions related to [your area of interest] that would advance the current state of knowledge in [specific subfield or topic]?”

Research Methodology & Data Collection Techniques

“Can you suggest the best research methodology and data collection techniques for studying [research topic] in [specific subfield or context], including their strengths, weaknesses, and when each would be most appropriate?”

Develop a Strong Introduction, Thesis Statement & Conclusion

“What are some effective strategies for developing a strong introduction, clear thesis statement, and convincing conclusion for my [research paper] on [research topic]? Please provide guiding questions and ideas on how to structure these elements to ensure they are effective and aligned with the research goals.”

Proofreading your Research Paper

“Proofread and edit my {Research Paper} for any grammatical, punctuation, repetitive words, and spelling errors. Please provide suggestions to improve the readability and flow of my research paper .”

Generating Synthetic Data

“I would like you to generate a dataset of {Dataset About?} with {Number of Records} synthetic records with the following characteristics.
{Name of Field} ({Data Type / Range }) … and so on.
{Special Instructions about Dataset}
The data should be realistic and plausible, not obviously fake or randomly generated. Format the output as a comma-separated values (CSV) file with a header row listing the field names and {Dataset Number} data rows.”


While ChatGPT can be a helpful resource, the actual human research and the researcher’s understanding and expertise in a particular subject remain essential for high-quality research. Here are the key takeaways from this chapter:

  • ChatGPT can help assist research tasks allowing you to focus more on the actual research and less on the other things. Sample prompts and examples are provided to demonstrate how it can assist at different stages of the research process.
  • Researchers should carefully frame prompts and provide enough detail and context to guide ChatGPT in generating relevant and valuable responses.
  • Researchers must take responsibility for the accuracy and reliability of their work. They should abide by the ethical considerations of using AI-based assistance in their research.

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11 Responses to Advanced Techniques for Research with ChatGPT

  1. Avatar
    Dr. R. Karim July 6, 2023 at 12:51 am #

    Very good and informative guides have been given for researchers. Thanks a lot.
    Dr. R. Karim, UPM, Malaysia

    • Avatar
      James Carmichael July 6, 2023 at 8:38 am #

      Thank you Dr. Karim for your feedback and support!

  2. Avatar
    Peter July 6, 2023 at 5:32 pm #

    Why would anyone try to use a tool made for writing fiction to do research!? It’s not what ChatGPT is for. It’s just going to make up data.

  3. Avatar
    Anggoro Yudho Nuswantoro July 14, 2023 at 1:11 pm #

    Hi, in the first case for data exploration, how to submit the dataset to ChatGPT for free user? To my knowledge, free user cannot install plugin. Thank

    • Avatar
      James Carmichael July 15, 2023 at 7:46 am #

      Hi Anggoro…That is currently the case.

  4. Avatar
    Hasan July 14, 2023 at 3:28 pm #

    Very informative post. Thank you!

    • Avatar
      James Carmichael July 15, 2023 at 7:45 am #

      Thank you for your support and feedback Hasan!

  5. Avatar
    Tom July 20, 2023 at 4:18 pm #

    ChatGPT3 does not seem to be able to collect citations and references correctly. At least in the medical research areas, a high percentage of the provided references are incorrect and even made up. The references look like probabilities of e.g. most common names, years and titles related to the topic under research. It is necessary to ask several times, e.g. to triple check the existence and correctness of a citation or reference. If someone has already asked to correct a small abstract, it can easily appear as an invented reference in the chat session. Therefore, it is recommended to find citations and references by normal means first, and then ask for GPT help.

    • Avatar
      James Carmichael July 21, 2023 at 9:20 am #

      Thank you for your feedback Tom! Let us know what you may find differntly if you have an opportunity to explore ChatGPT4!

  6. Avatar
    Tom July 28, 2023 at 10:07 pm #

    It looks like the GPT4 models are able to access online databases, but they do not handle the database records as a whole and start to “reassemble” the contents of a database record such as title, authors and abstract etc. after parsing from the online database. Therefore, it is still not accurate for scholarly purposes. The Bing AI (icon) in the Edge browser at least provides reference links directly for you to check.

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