Using ChatGPT to Conduct Meta-Analysis
Finding the best of product design best practices
Introduction
In an age where information is key, finding a consensus on best practices can often feel like navigating a maze with shifting walls. In an effort to pinpoint the most useful and widely agreed-upon strategies, I used ChatGPT, to analyze, compare, and synthesize knowledge from 26 expert articles on SAAS website data table design. Here’s my step-by-step approach.
Gathering and preparing the data
The first step was to identify and compile a list of articles that were comprehensive, credible, and diverse. I looked for articles that covered website data table design from different perspectives, including UX designers, UI designers, data analysts, and web developers. This ensured a balanced and diverse set of opinions and methodologies.
The selected articles were then converted into a plain text file format. This step involved carefully formatting each article with its title, author, date of publication, URL, and the main content. By preserving this metadata, I could later understand how different perspectives evolved over time and across different authors.
Training ChatGPT
I then started providing the content to ChatGPT, one article at a time. It’s important to note that this wasn’t a one-shot process. I had to iteratively communicate with the AI model, occasionally confirming the number of articles it had ingested. This helped ensure that the model was effectively digesting the content and keeping track of the data flow.
PROMPT: Thoroughly read, comprehend, and retain information from a series of 26 articles that I will be providing you 1 by 1. The articles are centered on data table design. Each article is accompanied by 5 data points: Title, Author, Date, URL, and Content. It is critical to retain the data points as you will be creating a citation. As you receive each article, acknowledge receipt with ‘Got it’.
… added this before each subsequent article
PROMPT: Here is another article. Remember, I need you to thoroughly read, comprehend, and retain information. I will be providing them one at a time, and after each one, simply acknowledge with ‘Got it’.
Analysis
After feeding the content, I prompted ChatGPT to perform an initial analysis. The AI model was instructed to critically examine, compare, and understand the key insights, methodologies, and conclusions presented in each of the articles. This is where the magic happened, as ChatGPT read, comprehended, and began making connections across the articles.
PROMPT: Analyze and summarize the key insights, methodologies, and conclusions from all 26 articles, identifying commonalities, divergences, and trends across the different articles. Provide a holistic overview of the articles without summarizing each articles individually. Consider the context and implications of each articles when drawing comparisons and identifying trends.
Finding commonalities and divergencies
Next, I asked ChatGPT to delve deeper into its analysis by identifying convergences and divergences in the dataset. The goal was to understand where experts agree (convergences) and where they disagree (divergences). This helped identify the most widely accepted best practices, as well as areas where there is still ongoing debate.
PROMPT: Write a summary for commonalities based on frequency. Include a bullet point for the top 5 commonalities. Write a summary for divergencies based on frequency. Include a bullet point for the top 5 divergencies
Trend Analysis
I anticipated observing patterns of agreement and disagreement in web design trends over the course of 23 years. By examining these trends over a substantial period, I aimed to gain insights into the evolution of table design and the factors that influenced its trajectory towards or away from consensus.
Prompt: I would like you to conduct trend analysis on the articles. Look for themes that are trending to toward consensus over the span of the articles. Look for commonalities that appear to be trending away from a consensus over the span of the articles. Lastly, look for trends that a human may not see. Summarize your findings and List 3 specific examples for each trend for each trend.
Creation of a Citation:
Lastly, I had ChatGPT generate a citation of its findings. This helped me ensure that the insights were derived from an analytically rigorous process.
Construct a citation list for a series of 26 website articles. Each citation should include the Title, Author, URL, and Date Published. Once all citations are created, arrange them in chronological order based on the Date Published.
Conclusion
In sum, the AI-driven analysis revealed that while there is a consensus on the importance of simplicity and clarity in website data table design, there are still points of contention, such as the use of zebra striping and how to display empty cells. These findings serve to highlight the importance of flexibility and user testing in website data table design to find the optimal balance for a given audience.
The lessons learned from this process underscore the potential of AI to enhance our understanding of complex topics and make informed decisions. It’s a testament to the power of technology in democratizing knowledge and shaping the future of web design.
