When I first tried ChatGPT, I used it like Google — just to find information. But then I started thinking, maybe ChatGPT could do more, like analyze real situations with human thinking. The problem was, I didn't know much about data modeling or coding, which I needed in order to really dig deep into analysis with ChatGPT.
Challenge
I didn't know much about data modeling or coding, which seemed important to really get the most out of ChatGPT. Without that technical expertise, I was limited to using ChatGPT at its lowest capabilities. I was stuck.
That feeling of being stuck didn't feel good to me. In fact, it motivated me to see how experts were utilizing ChatGPT in their workflows. However, I encountered a steep learning curve with advanced tools like TensorFlow, Python, Google Cloud and IBM Watson, realizing these offered more than I needed. This led me to a pivotal question: How can I leverage a tool like ChatGPT to tackle complex problems with human-like reasoning, without getting bogged down by the technical software and complex processes I don't need?
Reflecting on how to simplify the integration of data modeling without unnecessary complexity, I compared it to my old cable service. I had hundreds of channels but only watched a few, wishing instead for just my favorites and the option to change them as needed. This made me realize I wanted a similar, more straightforward approach to data — a way to focus on the essential, human-centric aspects without getting bogged down in everything else.
To overcome that challenge, I reverse-engineered the complex data modeling processes I had been learning about. I stripped away the most complicated and unnecessary steps and got back to the basics.
Through those efforts, I developed a process that doesn't require coding—just a single spreadsheet or a Google doc, ChatGPT, and a series of well-crafted perspective prompts.
This process allows me to create customizable data models, capable of offering expertise in analyzing data, forecasting risks and outcomes, performing research, simulating real-world situations, merging other knowledge profiles, and bringing human-centric thinking to life's most complex challenges. I call these knowledge profiles.
What sets knowledge profiles apart is their versatility. They can be tailored to offer general expertise or specialized insights. Think of them as personalized brains you can access whenever you need. They can represent thought leaders, like Albert Einstein or Maya Angelou, domain experts, such as Michael Jordan and Tom Brady or your supervisor on the job. You can even explore historical perspectives, like Christa McAuliffe's perspective on the intersection of historical research and space education.
Knowledge profiles have endless potential. They're also handy for enhancing business intelligence or creating customer knowledge profiles, like personas, to gain insights into your target audience's thoughts while using products and services.
Beyond just creating the knowledge profile, adjusting how the text is structured within your Google Sheet or document allows the information output by your knowledge profile to align with the subtleties and nuances of human thought processes.
Now that I've got an efficient, tried-and-tested, accessible process, I plan to bring this to developing societies, resource-constrained businesses, educational programs, and research communities. In the meantime, here are a few commonly asked questions about this process:
What types of data can knowledge profiles be used to capture and analyze? They can be used to analyze both unstructured and structured data.
How are knowledge profiles about to account for human thinking and cognition? A primary way is by structuring information and knowledge in a way that mimics human thought processes and decision-making.
How does ChatGPT handle and make sense of lengthy text passages? Consolidation-based data modeling and conversation-anchoring prompts allow ChatGPT to process and understand larger bodies of text without the need for additional Natural Language Processing tools.
Can I create and update as many knowledge profiles as I want, and how easy is it to make changes to them? You can create as many knowledge profiles as needed, and you can update or enhance the data model by adding text, videos, articles, emails, social media posts, research papers, podcast recordings, questionaries, cultural and demographical resources, and other unstructured data to it.
How easy is it for a non-tech-savvy person to use? The most challenging part is structuring your data model to provide the best answers and solutions when prompted. Once the data model is set up, you only need a tailored set of perspective prompt guidelines to extract information from the data model. That part can be learned in minutes.
Can I help you set up a knowledge profile?
If you have questions or need a bit of assistance setting up your knowledge profile, I'd love to help! Connect with me on LinkedIn and let's start a conversation!