Not just looping, you could do a parallel graph search of the solution-space until you hit one that works.
replyInfinite Monkey Theory just reached its peak
replyYou could also parse prompts into an AST, run inference, run evals, then optimise the prompts with something like a genetic algorithm.
replyAnd then it's slow again to finally find a correct answer...
replyIt won't find the correct answer. Garbage in, garbage out.
replyThis doesn't work. The model outputs the most probable tokens. Running it again and asking for less probable tokens just results in the same but with more errors.
replyDo you not have experience with agents solving problems? They already successfully do this. They try different things until they get a solution.
replyAgreed, this is exciting, and has me thinking about completely different orchestrator patterns. You could begin to approach the solution space much more like a traditional optimization strategy such as CMA-ES. Rather than expect the first answer to be correct, you diverge wildly before converging.
replyThis is what people already do with “ralph” loops using the top coding models. It’s slow relative to this, but still very fast compared to hand-coding.
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