Stop Trusting, Start Testing: Taking Control of Your AI Tools

When we blindly trust AI, we risk embarrassing errors—like the infamous suggestion to use glue to stick cheese on pizza or the chatbot demo that incorrectly credited the James Webb telescope with a milestone achieved decades earlier. 

To truly take control, we must stop assuming AI is magic and start treating it like any other tool: subject to reliability testing. This approach is helpful both when the AI model you’ve been using is updated as well as when you’re trying out a new AI model. 

Because the kind of testing we’re discussing here is limited to a “gut check” on your specific use of a specific  AI tool, it doesn’t require an entire dedicated Quality Assurance (QA) team. 

Instead, you can use a simplified “10-minute experimentation” to gauge your model’s performance for smaller, personal, or low-risk scenarios. This helps you quickly gauge your models’ strengths, identify their weaknesses, and determine when human intervention is necessary. 

Here is a simple approach that will help you move from guessing to knowing:

• Establish a Baseline: Start small. If you use AI to draft emails, take a standard request and feed it to whichever model you use (be it Gemini, ChatGPT, Claude, etc.) multiple times. Does it consistently generate a useful draft? If the quality varies wildly with the exact same input, you know you can’t rely on it without checking its work.
• Vary the Context: Once you have a baseline, change the type of work. Perhaps the model excels at drafting informal meeting notes but hallucinates when handling precise technical specifications for engineering teams. Testing these boundaries helps you map the model’s “safe zones” for use.
• Increase the Load: Test how the model handles volume. AI performance might be great when given two bullet points of context but degrade rapidly when asked to synthesize a dozen competing ideas. By spotting where performance drops, you learn when to step in and take over.

The Bottom Line: Testing isn’t just about catching errors; it’s about understanding performance so when you put confidence in an AI model it’s justified by evidence. By expending a little effort to “poke” these models, you move from hoping for a good result to knowing when to use the tool—and when to trust your own judgment instead.

Brian Vickers serves as Principal Data Scientist for Data and AI at MANTECH. Contact him via AI@MANTECH.com.