What If AI Gets It Wrong? A Simple Framework for Checking AI’s Work

What if AI makes a mistake?

It will. But that’s okay.

That’s because AI isn’t meant to replace your judgment. It’s meant to accelerate it. The key is knowing how to stay in the driver’s seat.

Your job is to review and guide what AI creates the same way you’d review the work of a new employee. Here’s a simple, three-step framework for doing just that.

The “Check, Cross-Check, Confirm” Framework

1. Check

Start with a quick read for anything that jumps out:

• Are there obvious factual errors or missing context?
  
  
• Does it sound too confident about something you’re not sure is true?
  
  
• Does it use the right tone or terminology for your audience?

Think of this as your first pass: You’re not doing a deep fact check yet, just scanning for what feels off. Your intuition is often your best early warning system.

2. Cross-Check

Next, let AI help you verify itself. Ask it questions like:

• “Can you show your sources for this?”
  
  
• “What’s your reasoning behind that claim?”
  
  
• “You said [insert quote] … what did you mean by that?”

Modern AI models can explain their reasoning or regenerate output using a different method. This is like asking a colleague to walk you through their thinking—it either reinforces confidence or surfaces errors.

3. Confirm

Finally, use your own domain knowledge or a trusted source to verify key details. That might mean double checking a number against your own data, consulting a reliable reference to confirm industry standards, or simply asking a teammate who knows the subject best.

AI can draft, summarize, and suggest—but you decide what’s right for your context. Think of this as your “final approval” step before you send, publish, or do anything with the AI output.

Example: The 30-Second Email Fix

Let’s say AI drafts an email to a client and includes this line:

“We increased customer retention by 40% last quarter.”

That sounds great … but it’s not true. Here’s how you’d catch it fast:

• Check: You notice the number seems high compared to your last report.
  
  
• Cross-Check: You ask AI, “Where did that 40% figure come from?” It admits it generated it based on similar case studies.
  
  
• Confirm: You open your analytics dashboard—the real number is 22%. You edit and send the email knowing the content is accurate.

In under a minute, you’ve turned a potential credibility issue into a demonstration of diligence, without sacrificing the productivity gains of using AI to help you draft your email.

Pro Tip: Let AI Audit Itself

You can also use AI as your second set of eyes, as long as you still verify afterwards. Try prompts like:

• “What might be wrong in this summary?”
  
  
• “List any claims that need verification.”
  
  
• “Does anything in this answer sound outdated or inconsistent?”

You’ll be surprised how effectively AI can critique its own work when asked the right way.

Build Checkpoints Into Your Workflow

Don’t leave accuracy to chance—design for it. Build deliberate checkpoints into your AI workflow so review becomes a habit, not an afterthought. Over time, you’ll notice you’re catching fewer and fewer issues. Not because you’ve stopped checking, but because both you and your AI have learned what “right” looks like.

That’s how trust is built: gradually, through structure and consistency, until accuracy becomes the default.

You’re still the decision maker. AI just helps you get there faster.

Mistakes are inevitable, but when you have a simple review process in place, they become manageable, not scary. The more you work with AI, the more natural this rhythm becomes: Check. Cross-Check. Confirm.