A developer shares a production incident where an AI-written rate limiter silently swallowed errors, then outlines a systematic testing process for AI-generated code. Key strategies include: enabling TypeScript strict mode with noUncheckedIndexedAccess, using ESLint/Biome for static analysis, writing adversarial edge case and error propagation tests before reading AI output, running real integration tests against staging environments, and using property-based testing with fast-check to catch input-space blind spots. The post also describes a workflow where test specifications are written before prompting the AI, edge case tests are written while the AI codes, and static analysis plus integration tests serve as hard merge gates.
Table of contents
Why AI Code Needs Different Testing HabitsThe Testing Gap in AI-Assisted DevelopmentStatic Analysis FirstTesting What AI Gets WrongIntegration Tests for AI-Written ModulesProperty-Based Testing for Complex LogicThe Code Review Step That Actually MattersBuilding the Testing Habit Into Your WorkflowThe AI Evals ConnectionWhat to Do Right NowSort: