Building Production-Ready AI Features: A Senior Developer's Playbook

Every engineering team is prototyping with AI right now. The demos are impressive. The production deployments are not. The gap between a compelling proof-of-concept and a reliable, maintainable AI feature is where most teams are quietly struggling , and where most of the interesting engineering problems actually live. This is what Cidersoft's senior engineers have learned shipping AI features in production environments over the past two years. The Demo Trap AI prototypes are dangerously easy to build. Wrap a GPT-4 API call in a few lines of Python, hook it to a frontend, and you have something that looks production-ready in an afternoon. The problem surfaces at scale: inconsistent outputs, no error handling, no fallbacks, no observability, and prompt logic scattered across codebase with no versioning. Production AI features require the same engineering rigor as any other system-critical component , plus a new category of problems that most teams haven't encountered before. 1. Treat Pro