别只把AI当打字机。试试这5条实操，让AI帮你还原真实工作流，更快拿到靠谱结果。
Kolt Regaskes指出AI的真正价值不在于提笔速度，而在于缩小反馈循环。他提出了5条具体操作：从明确成果而非模糊提示出发；让AI通过检查表、例稿等方式自我校验；将任务拆分为研究者、起草者、审稿者、编辑者角色；用文档存储上下文而非依赖记忆；设定升级规则，AI卡壳2-3次后切换模型或简化步骤。这套方法能帮助知识工作者以“推理速度”产出决策、总结和计划，减少往返错误。
The big shift is this - AI is not just about writi…
The big shift is this - AI is not just about writing faster, it is about closing feedback loops faster.
- Start with a clear outcome, not a vague prompt - what does “done” look like in plain English - Give the agent a way to check itself - a checklist, rubric, example output, or source to compare against - Break work into roles - researcher, drafter, reviewer, editor (you can use different models/tools for each) - Store shared context in simple docs, not your memory - notes, SOPs, project briefs, decisions - Add an escalation rule - if the agent gets stuck after 2-3 tries, switch model, switch role, or simplify the task. Even skipping a step can help.
For knowledge workers, “shipping at inference speed” means producing decisions, summaries, plans, and assets faster with fewer back-and-forth mistakes. Small loops win.