As we’ll see, generative AI also introduces new forms of technical debt, which have implications for how we maintain our systems in production. Most importantly, generative AI introduces unique sources of technical debt that can accumulate quickly if not properly managed, including:How are Machine Learning Developers Allocating Their Time Differently in GenAI Projects? Gathering response quality feedback can be done via a simple UI hosted on Databricks Apps that calls the MLflow Feedback API. To make the process easier, developers can use Databricks Data Quality Monitoring to track model quality metrics, input data quality, and potential drift of model inputs and predictions within a holistic framework. Testing is often more time-consuming in generative AI applications, for a few reasons:AI Technical DebtTechnical debt builds up when developers implement a quick-and-dirty solution at the expense of long-term maintainability.