I would like to propose a research direction for ChatGPT: learning generalized technical knowledge from successfully resolved real-world troubleshooting experiences.
Today, when a user encounters an obscure technical problem, the solution may be discovered through interaction with ChatGPT, even though that solution does not exist in official documentation or elsewhere on the web. However, that practical discovery largely remains confined to the individual conversation.
Could ChatGPT instead extract a privacy-preserving, generalized “technical lesson” from such experiences?
The concept would be:
Real-world troubleshooting → generalized technical lesson → independent validation → collective technical knowledge
For example, if a particular combination of an iPhone setting, an application, a network condition and a VPN configuration repeatedly causes a specific problem, and disabling the VPN repeatedly resolves it, the system could eventually recognize this as a technical pattern.
The important point is that this would NOT mean exposing or simply remembering individual users’ conversations. Personal and identifying information would be removed or abstracted, and confidence in a technical lesson could increase only when similar patterns are independently reproduced.
This could create a new layer of knowledge between conventional model training and individual conversation memory: privacy-preserving collective practical knowledge derived from real-world problem solving.
It could be especially valuable for obscure software bugs, undocumented compatibility issues, unusual configuration problems, networking issues, and other situations where practical experience is richer than official documentation.
In the long term, an AI assistant could potentially learn not only what has been documented, but also what has repeatedly been discovered and verified in practice.
I believe this deserves investigation from the perspectives of privacy, knowledge extraction, validation, confidence estimation, and continual learning.
I would be interested to hear whether OpenAI or other researchers have considered a similar approach.