Ground your LLM. Tests, documentation, give it many ways to run the thing its reasoning about. It needs to be able to test its hypotheses on its own.
Take yourself out of that loop so you only find out once it's sure.
Hm. Two orthogonal properties! This sounds like a 2x2 matrix!
Let's swap hard/easy around & explore the 4 possibilities...
There are domains with sharp delineation and hard verification; they are not at risk until AI gets much better. Humans operate in these domains by applying tremendous deep thought and subjective judgment - our superpower.
Domains with soft delineation and easy verification are most at risk: "it's a picture of a cat" remains true through a wide range of perturbations - eg. skewing the image or moving it across a pixel or correcting its white balance or even changing the cat. AI music? Lots of domains already solved by AI here but they're also not that meaty.
My prediction is the next interesting stuff will happen where verification is hard but there's no sharp delineation. It's the world of "I'll know it when I see it". Good customer service?
I keep thinking about the c compiler implementation that anthropic shared earlier in the year that had all the requirements you mention and arguably wasn’t that great.
OBVIOUSLY, the compiler experiment was just a cringe pr stunt. But it has a point: everything works better with a good testing loop, and compilers always have one by thr nature of the work they do
No amount of tokens can come close to my hourly rate.