AI outputs cannot be trusted. Unless you change the way you work.
We need to get better at “pressure testing” our ideas.
Here are three approaches I’m trying in our practice.
- Use a Red Team
AI produces plausible output rapidly – but we should not accept that uncritically.
Nominate a Red Team – people who try and tear apart what’s produced and test if the logic and assumptions still hold true.
Find the flaws before a customer does.
- Use AI cross checks
AI tools are cheap right now. You can feed the output of one into another and ask for a review.
It’s an easy way to validate the work.
Of course, both AI’s can hallucinate, but if they both agree on the key points, that gives you some confidence.
And humans make mistakes too. In one of my early posts I corrected AI output and got the message wrong. The AI was right.
Verify, then trust.
- Design defensively
As AI produces more of my code, I spend more time building tests.
For example, it’s easy to introduce errors into spreadsheets – and everyone uses spreadsheets all the time to collect and analyse raw data.
So I build in cross checks, error reviews, and comparative analyses – all to help me get confidence that what I’m working on is correct.
Test everything.
Do you have any other techniques you’re using as you integrate AI into your work?
Handcrafted insight 