The Pre-Flight Prompt: 5 Checks Before You Let AI Touch Production Code

AI-generated code ships fast. That's the feature and the risk. I've seen AI assistants produce code that passes tests, looks clean, and still breaks in production because of assumptions no one questioned. After dealing with enough of these, I built a pre-flight checklist: five checks I run before any AI-generated code touches production. Why You Need a Pre-Flight Here's what AI assistants do well: generate syntactically correct code that handles the happy path. Here's what they reliably miss: Environment-specific behavior (local vs. staging vs. production) Concurrency and race conditions Failure modes they weren't explicitly told about Security implications of the approach they chose Performance at production scale A pre-flight catches these before your users do. The 5 Checks Check 1: "What assumptions did you make?" Before reviewing any code, I ask: List every assumption you made while writing this implementation. Include assumptions about: - Input data (types, ranges, nullability) -