hey, I'm the lead engineer on this project! I love that you did this :)
our experience thus far is that GPT4 definitely needs help when it comes to datasheet-like data. it's great at broad "functional" strokes, but details and particularly numerical details are not its forte.
I think the real solution is to manually convert component datasheets to spice models (I think there are already commercial databases of these), simulate the circuit, and then get a language model like GPT-4 propose changes to the circuit to make it perform better.
The real question becomes how to interface the spice model and the language model - do you for example let it connect a virtual oscilloscope to any node, and give the language model the results back as plain numbers?
Comments
hey, I'm the lead engineer on this project! I love that you did this :)
our experience thus far is that GPT4 definitely needs help when it comes to datasheet-like data. it's great at broad "functional" strokes, but details and particularly numerical details are not its forte.
we're working on it!
I think the real solution is to manually convert component datasheets to spice models (I think there are already commercial databases of these), simulate the circuit, and then get a language model like GPT-4 propose changes to the circuit to make it perform better.
The real question becomes how to interface the spice model and the language model - do you for example let it connect a virtual oscilloscope to any node, and give the language model the results back as plain numbers?
Our thinking has been the same
Give the LLM the ability to run the simulator and optimize the solution based on the outputs
Lots of amazing opportunities ahead