But when we think of AI advancements, it often feels like it’s all systems go. However, I’m curious about the role meticulous bug tracking plays in the development of these models. In my experience as a QA engineer, I’ve seen first-hand how a single overlooked issue in the code can skew the model’s outputs. Does anyone have insights or examples where efficient bug tracking made a significant difference in AI testing?