Why it matters
Lee Robinson discusses recursive model improvement in the context of Cursor’s AI-native development work, focusing on how product use, model behavior, and engineering feedback can inform successive system improvements.
My takeaway: Improvement loops are only trustworthy when feedback is measurable. Teams should separate user adoption from quality, retain regression sets, and review how production feedback is selected before it influences models or prompts.