Grounded Inference: Principles for Deterministically Encapsulated Generative Models

The paper introduces a foundational framework for integrating generative models into traditional computational systems, defining four specific primitives for deterministic encapsulation of probabilistic models. It also identifies two anti-patterns prevalent in the industry, aimed at guiding engineers in mitigating risks associated with AI integration. This framework is significant for practitioners as it lays the groundwork for developing safer and more effective generative model interfaces, enhancing the reliability of AI applications in established systems.