- Writing: emails, documentation, marketing - Write a bunch of unstructured skeleton of information. Add a prompt about the intended audience and a purpose. Possibly ask it to add some detail.
- Coding: Especially things like "Is there a method for this in this library" - a lot quicker than browsing through documentation. Some errors - copy-paste the error from the console, maybe a little bit for context, and quite often I get the solution.
And API based:
- Support bot
- Prompt engineering of some text models that normally would require labeling, training, and evaluation for weeks or months. A couple of use cases - unstructured text as an input + prompt, JSON as an output.
A lot of very varied things so it’s hard to remember. Yesterday I used it extensively to determine what I need to buy for a chicken coop. Calculating the volume of concrete and cinder blocks needed, the type and number of bags of concrete I would need, calculating how many rolls of chicken wire I would need, calculating the number of shingles I would need, questions on techniques, and drying times for using those things, calculating how much mortar I would need for the cinderblocks (it took into account that I would mortar only on the edges, the thickness of mortar required for each joint, it accounted for the cores in the cinderblocks, it correctly determined I wouldn’t need mortar on the horizontal axis on the bottom row) etc. All of this, I could’ve done by hand, but I was able to sit and literally use my voice to determine all of this in under five minutes.
I use DALLE3 extensively for my woodworking hobby, where I ask it to come up with ideas for different pieces of furniture, and have constructed several based on those suggestions.
For work I use it to write emails, to come up with skeletons for performance reviews, look back look ahead documents, ideas for what questions to bring up during sprint reviews based on data points I provide it etc.
Not OP but I used it very successfully (not OpenAI but some wrapper solution) for technical/developer support.
Turns out a lot of people prefer talking to a bot that gives a direct answer than reading the docs.
Support workload on our Slack was reduced by 50-75% and the output is steadily improving.
- Writing: emails, documentation, marketing - Write a bunch of unstructured skeleton of information. Add a prompt about the intended audience and a purpose. Possibly ask it to add some detail.
- Coding: Especially things like "Is there a method for this in this library" - a lot quicker than browsing through documentation. Some errors - copy-paste the error from the console, maybe a little bit for context, and quite often I get the solution.
And API based:
- Support bot
- Prompt engineering of some text models that normally would require labeling, training, and evaluation for weeks or months. A couple of use cases - unstructured text as an input + prompt, JSON as an output.