I wanted to start a broader ideas worth trying thread around AI Testing & Debugging.
A useful angle is learning curve, but it also connects with deployment. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where AI Testing & Debugging is genuinely useful and where it still needs improvement.
A useful angle is learning curve, but it also connects with deployment. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where AI Testing & Debugging is genuinely useful and where it still needs improvement.