The rise of AI-assisted development has transformed coding practices, requiring a balanced approach that combines AI code generation with traditional quality control measures. The article outlines how
developers can adapt their workflows to leverage AI's benefits while maintaining code quality through testing, peer reviews, and static analysis.
Reasons to Read -- Learn:
how AI-assisted development is transforming traditional coding workflows, with concrete examples like Google's 25% AI-generated codebase
specific challenges unique to AI-generated code, including non-deterministic behavior, hidden biases, and structure inconsistencies, helping you better evaluate and manage AI coding tools
practical strategies for maintaining code quality in an AI-assisted development environment, with a detailed six-step workflow that combines both human oversight and automated tools
5 min readauthor: Mete Atamel
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