Many developers experience the 'demo works, production fails' phenomenon when integrating LLMs into real applications. This post, written from a Java developer's perspective, highlights that the biggest challenges aren't model accuracy but operational concerns like permission management and audit logging. In a demo, you can ignore who has access to what and how actions are tracked. In production, these become critical: you need fine-grained permission controls to ensure the LLM doesn't perform unauthorized actions, and comprehensive logs to trace decisions and debug issues. The author shares practical advice on implementing these systems, emphasizing that permission logs are the real gatekeeper for safe and reliable LLM applications. This is a valuable perspective for developers transitioning from traditional software to AI-powered systems.
A Java developer's lessons on moving LLM apps from demo to production, focusing on permissions and logging as critical success factors.