I see a few comments about how the interface should be better.
I would argue that the current interface is broad and can be adapted to a wide range of needs.
The real value I see (and an area I've been exploring recently) is creating abstractions around prompt engineering.
The idea is that at the moment, the better the prompt -> the more relevant the output.
However businesses that act as proxies for chatgpt can take user input -> inject it into their prompt-engineering system -> deliver better results.
As a basic example:
1) You provide an application that helps people maintain their cars
2) You have a freeform question input about specific car maintenance tasks
3) Knowing certain prompt-segments that typically lead to better results for the category of "novice car maintainer", you parse their question, upgrade it with your prompt-generator, then send that question to chatgpt
Additionally, you can go even further by generating the information _before your customers even ask_.
Ask chatgpt what the most common questions are about each piece of car maintenance (could be installation instructions, cost of parts, etc) and have these available straight in the application.
It's as if chatgpt is the database, and prompt engineering systems are the query-optimizers. I know certain things I can alter about a prompt to increase the relevance and usefulness of the results to my target audience.
Additionally, it will be interesting to see how people use learning methods to auto-generate the best prompts (it's a bit of machine-learning on machine-learning happening). I've spoken to one person working in this space, and it's interesting how effectively the outer layer is reversing the prompt-parsing layer buy scoring the end-to-end results.
Currently working with a couple of people in this space and it's exciting the types of results that user-focused engineering can bring to an improved user-experience.