Key Takeaways

  • When AI like Claude delivers an output you don't want, don't just re-prompt; confront it directly by asking, “You didn't do what I wanted you to do. Why is that?”
  • Treating AI interaction like managing an employee helps uncover misunderstandings and improve future performance, according to Nicole Ruiz.
  • Getting feedback from the AI itself allows you to understand its reasoning, even if flawed, and provide clearer criteria for the next attempt.
  • This direct feedback loop reduces wasted time and improves AI effectiveness by identifying where your instructions were unclear or misinterpreted.

The Method: Interrogate Your AI

Founders often hit a wall with AI tools like Claude. They craft a prompt, the AI spits out something useless, and the first instinct is to try another prompt, hoping for the best. Nicole Ruiz, a builder who uses AI to manage everything from personalized shopping assistants to product returns, calls this out as a rookie mistake. Her advice is counter-intuitive and powerful: when AI misses the mark, treat it like an employee who didn't quite get the memo.

“I think when I'm talking to friends about using Claude because I think people are first using it, they'll be like, 'Claude did not do what I wanted it to do. What do I do next?'” Ruiz explains. “And I think you people always underestimate the amount you can just be like, 'You didn't do what I wanted you to do. Why is that?'”

This isn't about being confrontational. It's about opening a dialogue. Instead of silently iterating, you're asking the AI to self-diagnose. Ruiz points out that this mirrors human management: “Again, like as you've talked with a lot of the people on your show about, like it is often like managing an employee.” When an employee misunderstands a task, a good manager doesn't just reassign it with slightly different words. They ask for an explanation. “Understanding where they're coming from, giving them a little bit of space to explain so that you understand when what went wrong can be very helpful to them and say, 'Please don't do that again. Here's the new criteria and here are the new guidelines,'” Ruiz adds.

By asking "Why?", you push the AI to reveal its internal logic, its misinterpretations, or the gaps in its understanding of your original prompt. Maybe it prioritized speed over detail, or missed a subtle keyword. This transparency is gold. Once you grasp why it failed, you can then give precise, targeted feedback. It turns a frustrating iteration loop into a structured debugging session.

Ruiz doesn't mince words: “You can just be really really honest and say, 'That was not what I wanted at all. I think you misunderstood. Where did we go wrong here?'” This directness saves time and mental energy, steering the AI towards better output faster. It's about training your AI, not just prompting it.

Where This Breaks Down

This method shines when your initial prompt has some clarity, but the AI's interpretation diverged. It presumes the AI has enough contextual awareness to explain its reasoning. If your prompt was incredibly vague or open-ended, the AI's explanation of its "failure" might be equally unhelpful, a kind of digital shrug. For instance, asking "Write me a good blog post" and then "Why wasn't that good?" likely won't yield actionable insights, because "good" is subjective without further definition.

It also works best with more capable models like Claude or GPT-4. Simpler, less sophisticated models might struggle to articulate their reasoning or offer truly insightful feedback, instead generating generic excuses that don't help refine your input. Don't expect a nuanced self-reflection from a basic chatbot. Furthermore, if the AI's failure stems from a lack of up-to-date information or an inherent limitation in its knowledge base, no amount of "why" will fix it.

What to Do With This

The next time your AI output isn't quite right, resist the urge to immediately tweak your prompt. Instead, explicitly ask, "Why did you generate this result? What part of my instructions did you prioritize or misunderstand?" Then, based on its explanation, provide precise, updated guidelines: "Okay, I see. Next time, prioritize [specific criterion] and make sure to exclude [specific element]." This direct, iterative feedback will dramatically cut down your revision cycles and sharpen your AI's utility.