Ever thought about generating a 10-meter human or someone who's lived 500 years? Sounds absurd, right? Yet this is exactly what happens when building synthetic datasets without proper constraints.

Here's the trap: if you don't establish realistic boundaries for your data ranges, you end up defining them way too broadly. The outcome? Your training set gets flooded with garbage data—edge cases that could never exist in the real world.

Then you feed all this noise into your AI model. Result: wasted computational resources, longer training cycles, and a model that learns patterns from invalid examples instead of meaningful data. It's like teaching someone to drive using instruction manuals from both cars and airplanes mixed together.

The lesson? When generating synthetic data for model training, hard constraints based on reality aren't just helpful—they're critical. Define what's actually possible first. Everything else is just junk.