It's pretty cool how it works, too: the OpenAI Assistant uses the LLM to take your human instructions like "how many builds is in the dataset?" and translate that into Python code which is run in a sandbox on OpenAI compute with access to the dataset you've uploaded.
Under the hood everything is just numpy, pandas and gnuplot, you're just using a human interface to a Python interpreter.
We've been building an AI feature into our product recently that behaves like this and it's crazy how good it can get. I've done a lot of data analysis in my past and using these tools blew me away, it's so much easier to jump into complex analysis without tedious setup.
And a tip I figured out halfway through: if you want to, you can ask the chat for an iPython notebook of it's calculations. So you can 'disable autopilot' and jump into manual if you ever want finer control over the analysis it runs. Pretty wild.
I also got surprised about using it for this kind of work. I don't have access to copilot and gpt-4 at work but my first instinct is to ask, did you double check its numbers?
Knowing how it works now makes more sense that it would make less mistakes but I'm still skeptical :P
