I actually have a bayesian stochastic process model for LLM codegen that incorporates the noisy channel coding theorem, it turns out that just like noisy communications channels can be encoded to give arbitrarily low error rate communication, LLM agents workflows can be coded to give arbitrarily low final error rate output. The only limitation on this is when model priors are highly mis-aligned with the work that needs to be done, in that case you need hard steering via additional context.

Which model gives you creatives outputs ?