Engineering for Integrity in the Age of Hallucinating Models: An AI-Powered Exam System Case Study
AI feels magical the first time you use it. You type a prompt, hit enter, and something useful appears almost instantly. It feels like the system understands you. It feels like the hard part is already solved.
That feeling doesn’t last long.
What comes next is not a crash or a loud failure. It’s something quieter. The system keeps working, but small things start slipping. Numbers look right but are formatted wrong. Fields go missing. Structures change slightly from one response to another. Nothing looks broken at first, but everything becomes harder to trust.
We ran into this while using AI to generate structured data for APIs. One example still stands out. The system expected a value like 120.50. The AI returned 120.5. Same number, same meaning, but the system rejected it. That tiny difference broke the flow. It took time to even realize what the problem was because nothing...
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