A developer shares their comprehensive workflow for using LLMs in software development, detailing processes for both greenfield and legacy code projects. The approach involves three main steps: idea honing, planning, and execution, with practical tools and prompts for efficient code generation and review.
A critical analysis of how AI tools are impacting junior developers' fundamental coding knowledge and understanding, highlighting concerns about shallow learning. Developers are shipping code faster with AI assistance but lack deep comprehension of programming concepts and problem-solving skills that traditionally came from hands-on experience.
An experienced staff engineer shares practical insights on leveraging LLMs effectively in software development, highlighting use cases from code completion to learning new domains. The author emphasizes using AI tools like GitHub Copilot strategically, particularly for boilerplate code and learning scenarios, while maintaining human oversight for critical tasks.