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#2The two skills that actually matter in 2025 aren’t prompting tricks or jailbreaking (everyone has those now). They’re (1) turning vague ideas into ruthless evaluation benchmarks and (2) knowing exactly when the model is hallucinating vs reasoning.
Which one do you think is harder to teach people in practice?
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#3Happy to dive deeper into any of these if it’s useful.
I’ve been testing these workflows daily (decomposition, iterative refinement, reasoning passes, compression loops), so if anyone wants concrete examples or wants to compare approaches, I’m happy to share and discuss.
Re: undefined
#4The two skills that actually matter in 2025 aren’t prompting tricks or jailbreaking (everyone has those now). They’re (1) turning vague ideas into ruthless evaluation benchmarks and (2) knowing exactly when the model is hallucinating vs reasoning. Which one do you think is harder to teach people in practice?
Great question. In practice, (1) is harder for most people.
Turning vague ideas into evaluation benchmarks requires a level of procedural thinking that many non-technical users don’t naturally apply. You need to define constraints, success criteria, edge cases, and failure modes — basically treating any task like a mini-spec. Once people see that framing, their results improve dramatically.
Detecting hallucinations vs reasoning (2) is also important, but in my experience it becomes easier once users adopt a habit of forcing the model to externalize its reasoning (step-by-step assumptions, uncertainty estimates, alternative paths). When the chain of thought is explicit, hallucinations become much more obvious.
Curious how you see it from your experience.