Production-ready, AI-assisted Development and Workflows

A Pluralsight course author shares practical guidance on making AI-generated code production-ready. Key concerns covered include code review challenges (AI increases volume but misses business context), testing pitfalls (high coverage can create false security if AI misunderstands requirements), technical debt risks (AI tends to over-complicate and lacks full codebase context), and the danger of 'cognitive surrender' where developers lose ownership and understanding of their own systems. The post advocates for maintaining human oversight at each stage while leveraging AI's speed advantages.