We continuously explore tools that accelerate development and improve efficiency. Like many engineering teams, we adopted GitHub Copilot to assist in generating React components. However, we quickly encountered challenges: 🔹 Excessive prop drilling 🔹 Unstructured component design 🔹 Up to 30% slower rendering performance The issue stemmed from AI-generated code that lacked architectural context, leading to long-term maintainability concerns. To address this, our engineering team implemented a structured solution: ✔️ Developed custom ESLint rules based on internal code quality standards ✔️ Created a React Server Component template library using TypeScript for scalable, reusable components ✔️ Integrated Sourcegraph Cody for context-aware code suggestions aligned with our architecture These steps significantly improved both code maintainability and application performance. As AI continues to shape modern development workflows, it’s essential to pair automation with strong engineering practices and quality controls. Have you faced similar challenges with AI-generated code? Share your thoughts or experiences in the comments below
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