In this blog, I break down what “AI quality” really means and why simply saying an AI “works” isn’t good enough. I walk through the key metrics—like hallucination rate, task completion, latency, and trust—that actually define whether an AI is reliable and useful. I explain why traditional software measurements fall short and how we need to evaluate real outcomes instead of impressive-but-shallow outputs. By the end, I show how we can build AI that users can trust to work consistently, safely, and predictably.

AI Quality - Preventing Confident Lies

mmarriage