Stop Treating AI Like a Magic 8-Ball: A System for Better Collaboration
Published on 23.12.2025
Stop Yelling Instructions at a Confident Idiot
TLDR: Most people use AI like a Magic 8-Ball, giving it a vague prompt and hoping for a good result. This article, by Nick Quick, argues that the key isn't better prompts, but a better process. He proposes a simple three-step system—Feed, Reflect, Correct—to force the AI to ask clarifying questions, state its assumptions, and learn from your feedback, turning it from a "confident idiot" into a genuine collaborator.
Summary: This guest post from Nick Quick on the AI Maker newsletter delivers a much-needed dose of reality in the over-hyped world of AI prompting. The central argument is that we're using AI all wrong. We treat it like a new hire that we give a high-level task to and then walk away, providing no context, no examples, and no clarification. The AI, a "confident idiot," then proceeds to guess its way through hundreds of micro-decisions about tone, audience, and intent, producing a polished but ultimately useless result that we have to rewrite anyway.
The author argues that the endless cycle of prompt-and-pray is a waste of time. The solution isn't another "Ultimate Prompt Pack," but a fundamental shift in our interaction model. Instead of treating the AI like an oracle, we should treat it like a junior collaborator who needs onboarding. The proposed "Feed, Reflect, Correct" system is designed to facilitate this. It’s a simple but powerful loop that forces an intake conversation that AI doesn't have by default.
First, you Feed the AI your task, but with a critical instruction: before generating anything, it must ask you 3-5 clarifying questions about the audience, desired outcome, and potential pitfalls. Crucially, these questions must be aimed at understanding your intent, not showing off its own knowledge. Second, after you answer, you make it Reflect. You ask it to state its assumptions, its current understanding of what matters most, and what it's still uncertain about. This step is designed to surface the hidden, incorrect assumptions that would otherwise derail the entire output. The author gives a great example of how this step saved him from writing an article for the wrong audience.
Finally, you Correct. When the AI does generate content, you don’t just fix the errors; you explain the principle behind the correction. By providing edits with a "why," you teach the AI your preferences and style. For example, changing "leverage AI capabilities" to "use AI" and explaining that you avoid corporate jargon teaches a reusable principle. This feedback loop compounds over time, making the AI a progressively better collaborator. The author acknowledges that this system is more work upfront, but it saves significant time and frustration by avoiding the endless cleanup and rewriting that results from the "Magic 8-Ball" approach.
Key takeaways:
- Stop treating AI like a mystical oracle and start treating it like a junior collaborator that needs guidance.
- The problem with AI output is often not the prompt, but the hundreds of un-asked questions the AI has to guess the answers to.
- A simple system of "Feed, Reflect, Correct" can transform your relationship with AI.
- Feed: Force the AI to ask clarifying questions about intent, audience, and constraints before it starts working.
- Reflect: Make the AI state its assumptions and understanding of the task to surface hidden misunderstandings.
- Correct: Provide feedback that explains the principle behind your edits to teach the AI your style for future tasks.
Tradeoffs:
- Initial Effort vs. Long-Term Efficiency: The "Feed, Reflect, Correct" system requires more initial setup and interaction for each task compared to a simple one-shot prompt. However, it sacrifices immediate speed for long-term efficiency, as it reduces the significant time spent on cleanup and rewrites, and the AI's performance improves over time.