Generative AI tools like GitHub Copilot and ChatGPT can boost developer productivity by 15–55%, but they introduce serious risks including security vulnerabilities, hallucinated dependencies, subtle business logic errors, and technical debt. The solution is combining AI assistance with established Agile practices: TDD catches incorrect implementations immediately by requiring tests before code; BDD creates human-readable business logic specifications that serve as AI prompts and validators; ATDD aligns AI output with stakeholder requirements; pair programming adds human oversight to AI-generated code; and CI pipelines automate all checks on every commit. Real-world examples from invoicing and compliance platforms show these practices catching tax rounding errors, incorrect discount logic, and hallucinated SDK methods before they reach production. Organizations should make TDD non-negotiable for AI-assisted development, update code review checklists for AI-specific risks, invest in fast CI infrastructure, and track defect rates in AI-generated vs. human-written code.
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The GenAI code quality crisis: Real-world issuesThe root cause: Speed without clear specificationSort: