What "done" means when you're shipping AI features (jeffgothelf.com)

The tech community is redefining what it means to be "done" when shipping AI features, moving away from traditional, deterministic definitions to a more nuanced understanding suited for probabilistic behavior. Unlike conventional software, where consistency is key, AI delivers varying outputs based on different contexts, prompting teams to embrace a calibration approach. This involves assessing a feature’s performance across a range of conditions, rather than as a binary pass-fail result based on predefined assertions. This shift is significant for AI/ML practitioners, as it emphasizes the need for acceptance criteria that reflect distributions of expected behavior, proactive triage strategies for potential issues, and clear rollback mechanisms in case of problems post-launch. By acknowledging the unpredictable nature of AI, teams can enhance user experience and operational resilience, ultimately leading to reduced errors, better customer satisfaction, and more deliberate feature rollouts. Emphasizing a culture of learning and adaptation over rigid checklists, this approach sets the stage for responsible and effective AI integration in tech products.