Is it possible to construct a control system where bad, fast and cheap can become good, fast, and cheap through repeated sampling and a strong spec/eval harness?
I am trying to keep an open mind with AI, but I also have little understanding of control theory, trying to learn.
You can, but you need to break the problem into much smaller tasks, then check those answers, and finally have a harness that handles all the context, task breakup, task definitions, and validations each round.
Depends on how many times you need to iterate to get the result you want. If you need to run the fast model 5 times to get the results you need, compared to 1-2 times for a smarter but slower model, you've just eroded any advantage that the speed gave you.
If the model provides me with bad results because it's dumb, I don't care how quickly it does it.
But there are lots of use cases where a relatively "dumb" model is good enough.
Is it possible to construct a control system where bad, fast and cheap can become good, fast, and cheap through repeated sampling and a strong spec/eval harness?
I am trying to keep an open mind with AI, but I also have little understanding of control theory, trying to learn.
You can, but you need to break the problem into much smaller tasks, then check those answers, and finally have a harness that handles all the context, task breakup, task definitions, and validations each round.
Smaller models seems to get stuck in "loops" when you try to "handle" them this way.
fast results that you need to verify are better than slow (allegedly better) results that you still need to verify. REPL vs batch.
Depends on how many times you need to iterate to get the result you want. If you need to run the fast model 5 times to get the results you need, compared to 1-2 times for a smarter but slower model, you've just eroded any advantage that the speed gave you.
So, as always: it depends on the use case.