Developing GPT User Guides Through Model Introspection
Content Strategy
Prompt Engineering
Prototyping
Few clues to help me interact with these...
Few clues to help me interact with these...
Few clues to help me interact with these...
The Problem
The Problem
The Problem
The GPT Store unleashed a flood of specialized AI tools, each with its own unique capabilities and optimal ways of working. But there's a problem: they all look exactly the same. Every GPT, whether it's built for coding, creative writing, or graphic design, presents users with an identical blank chat interface.
This creates a frustrating experience. Users have no way to know how one GPT might work differently from another. Even popular GPTs with millions of conversations provide minimal guidance on effective use. It's like being handed different specialized tools that all look the same, with no instruction manual for any of them.
The GPT Store unleashed a flood of specialized AI tools, each with its own unique capabilities and optimal ways of working. But there's a problem: they all look exactly the same. Every GPT, whether it's built for coding, creative writing, or graphic design, presents users with an identical blank chat interface.
This creates a frustrating experience. Users have no way to know how one GPT might work differently from another. Even popular GPTs with millions of conversations provide minimal guidance on effective use. It's like being handed different specialized tools that all look the same, with no instruction manual for any of them.
The GPT Store unleashed a flood of specialized AI tools, each with its own unique capabilities and optimal ways of working. But there's a problem: they all look exactly the same. Every GPT, whether it's built for coding, creative writing, or graphic design, presents users with an identical blank chat interface.
This creates a frustrating experience. Users have no way to know how one GPT might work differently from another. Even popular GPTs with millions of conversations provide minimal guidance on effective use. It's like being handed different specialized tools that all look the same, with no instruction manual for any of them.
The Design Challenge
The Design Challenge
The Design Challenge
How might we help users better understand and effectively engage with specialized GPTs?
How might we help users better understand and effectively engage with specialized GPTs?
How might we help users better understand and effectively engage with specialized GPTs?
While the final solution might involve deeper interface changes, I focused first on a low-scope idea I thought could provide immediate user value: automatically generated documentation. It felt like an approachable first step that could help users today while working toward more comprehensive solutions.
While the final solution might involve deeper interface changes, I focused first on a low-scope idea I thought could provide immediate user value: automatically generated documentation. It felt like an approachable first step that could help users today while working toward more comprehensive solutions.
While the final solution might involve deeper interface changes, I focused first on a low-scope idea I thought could provide immediate user value: automatically generated documentation. It felt like an approachable first step that could help users today while working toward more comprehensive solutions.
Discovery
Discovery
Discovery
Rough flowchart of my process
Rough flowchart of my process
Rough flowchart of my process
My exploration started simply: I asked various GPTs to tell me about themselves. These casual conversations revealed something interesting - the models could often articulate their own capabilities and intended use patterns quite well. This made me think there might an opportunity to use this introspection to improve the UX without requiring users to do the digging themselves.
My exploration started simply: I asked various GPTs to tell me about themselves. These casual conversations revealed something interesting - the models could often articulate their own capabilities and intended use patterns quite well. This made me think there might an opportunity to use this introspection to improve the UX without requiring users to do the digging themselves.
My exploration started simply: I asked various GPTs to tell me about themselves. These casual conversations revealed something interesting - the models could often articulate their own capabilities and intended use patterns quite well. This made me think there might an opportunity to use this introspection to improve the UX without requiring users to do the digging themselves.
Research & Analysis
Research & Analysis
Research & Analysis
Running the diagnostic prompt
To validate this initial insight, I developed a systematic approach to understanding GPT capabilities. I created a diagnostic prompt that could probe interaction patterns, specialized features, and key limitations and report back its findings.
To validate this initial insight, I developed a systematic approach to understanding GPT capabilities. I created a diagnostic prompt that could probe interaction patterns, specialized features, and key limitations and report back its findings.
To validate this initial insight, I developed a systematic approach to understanding GPT capabilities. I created a diagnostic prompt that could probe interaction patterns, specialized features, and key limitations and report back its findings.
Solution Design
Solution Design
Solution Design
Running the guide generator prompt
While the diagnostic approach proved valuable for research, the output was still too technical for everyday users. The next step was clear: create a more streamlined version that could generate user-friendly documentation.
I developed a second, simplified prompt that could transform the GPT's introspection directly into standardized, easy-to-follow guides focused on practical usage patterns.
While the diagnostic approach proved valuable for research, the output was still too technical for everyday users. The next step was clear: create a more streamlined version that could generate user-friendly documentation.
I developed a second, simplified prompt that could transform the GPT's introspection directly into standardized, easy-to-follow guides focused on practical usage patterns.
While the diagnostic approach proved valuable for research, the output was still too technical for everyday users. The next step was clear: create a more streamlined version that could generate user-friendly documentation.
I developed a second, simplified prompt that could transform the GPT's introspection directly into standardized, easy-to-follow guides focused on practical usage patterns.
Testing & Validation
Testing & Validation
Testing & Validation
Universal Primer GPT
Universal Primer GPT
Universal Primer GPT
I tested this approach with Universal Primer GPT, which provided an ideal case study given its large user base. The testing validated two critical aspects of the solution: 1) the model could effectively articulate its own design principles and optimal usage patterns, and 2) it could do so without exposing any proprietary system prompt details.
This approach creates value for everyone involved:
End users get clear guidance on how to effectively use each GPT
GPT creators can automatically generate user guides for their tools without exposing their proprietary prompt engineering work
OpenAI maintains a secure marketplace while improving user experience
The successful validation showed that automated documentation generation could be both feasible and beneficial for the entire GPT ecosystem.
I tested this approach with Universal Primer GPT, which provided an ideal case study given its large user base. The testing validated two critical aspects of the solution: 1) the model could effectively articulate its own design principles and optimal usage patterns, and 2) it could do so without exposing any proprietary system prompt details.
This approach creates value for everyone involved:
End users get clear guidance on how to effectively use each GPT
GPT creators can automatically generate user guides for their tools without exposing their proprietary prompt engineering work
OpenAI maintains a secure marketplace while improving user experience
The successful validation showed that automated documentation generation could be both feasible and beneficial for the entire GPT ecosystem.
I tested this approach with Universal Primer GPT, which provided an ideal case study given its large user base. The testing validated two critical aspects of the solution: 1) the model could effectively articulate its own design principles and optimal usage patterns, and 2) it could do so without exposing any proprietary system prompt details.
This approach creates value for everyone involved:
End users get clear guidance on how to effectively use each GPT
GPT creators can automatically generate user guides for their tools without exposing their proprietary prompt engineering work
OpenAI maintains a secure marketplace while improving user experience
The successful validation showed that automated documentation generation could be both feasible and beneficial for the entire GPT ecosystem.
The Artifacts
The Artifacts
The Artifacts
The experiment produced two distinct artifacts:
GPT Introspection Diagnostic Prompt - For systematically exploring GPT capabilities
Quick-Start User Guide Generator - For creating practical documentation
The results were promising. This approach created clear documentation that could help users understand not just what a GPT can do, but how to interact with it effectively—all while maintaining flexibility and scalability for different use cases.
(Follow the links to view both prompts in full with samples responses.)
The experiment produced two distinct artifacts:
GPT Introspection Diagnostic Prompt - For systematically exploring GPT capabilities
Quick-Start User Guide Generator - For creating practical documentation
The results were promising. This approach created clear documentation that could help users understand not just what a GPT can do, but how to interact with it effectively—all while maintaining flexibility and scalability for different use cases.
(Follow the links to view both prompts in full with samples responses.)
The experiment produced two distinct artifacts:
GPT Introspection Diagnostic Prompt - For systematically exploring GPT capabilities
Quick-Start User Guide Generator - For creating practical documentation
The results were promising. This approach created clear documentation that could help users understand not just what a GPT can do, but how to interact with it effectively—all while maintaining flexibility and scalability for different use cases.
(Follow the links to view both prompts in full with samples responses.)
Looking Forward
Looking Forward
Looking Forward
An exploratory demo I made in Figma
This was a focused, time-boxed experiment testing two key ideas: whether GPTs could effectively document themselves, and whether we could use that capability to improve the user experience. While both hypotheses showed promise, there's much more to explore.
I spent just a brief time (about 50 minutes) sketching out a few ideas for how this could be integrated into the GPT Store interface. Even this rough exploration revealed exciting possibilities for future work: automated guide generation during GPT creation, interactive onboarding experiences, and visual differentiation between GPT types.
The immediate goal was to validate that model introspection could serve as a practical foundation for improving the GPT Store's user experience challenges. The next challenge? Making it seamless for both creators and users.
This was a focused, time-boxed experiment testing two key ideas: whether GPTs could effectively document themselves, and whether we could use that capability to improve the user experience. While both hypotheses showed promise, there's much more to explore.
I spent just a brief time (about 50 minutes) sketching out a few ideas for how this could be integrated into the GPT Store interface. Even this rough exploration revealed exciting possibilities for future work: automated guide generation during GPT creation, interactive onboarding experiences, and visual differentiation between GPT types.
The immediate goal was to validate that model introspection could serve as a practical foundation for improving the GPT Store's user experience challenges. The next challenge? Making it seamless for both creators and users.
This was a focused, time-boxed experiment testing two key ideas: whether GPTs could effectively document themselves, and whether we could use that capability to improve the user experience. While both hypotheses showed promise, there's much more to explore.
I spent just a brief time (about 50 minutes) sketching out a few ideas for how this could be integrated into the GPT Store interface. Even this rough exploration revealed exciting possibilities for future work: automated guide generation during GPT creation, interactive onboarding experiences, and visual differentiation between GPT types.
The immediate goal was to validate that model introspection could serve as a practical foundation for improving the GPT Store's user experience challenges. The next challenge? Making it seamless for both creators and users.
Creating intuitive software and content fostering the creativity in everyone.
Available for Hire
©2024 Patrick Morgan
Creating intuitive software and content fostering the creativity in everyone.
Available for Hire
©2024 Patrick Morgan
Creating intuitive software and content fostering the creativity in everyone.
Available for Hire
©2024 Patrick Morgan