A developer shares a simple but effective two-step prompting strategy for working with AI coding assistants, specifically Cursor. The approach involves requesting an overview before any code generation, which helps catch misunderstandings and requirement gaps early in the development process.
Present an overview of what you will do. Do not generate any code until I tell you to proceed!
Always ask the AI to present an overview of its planned implementation before generating any code
Review AI's understanding and refine requirements before proceeding with code generation
Verify that AI has acknowledged all necessary files and dependencies before code generation
Avoid letting AI generate code immediately without understanding its planned approach
Avoid assuming AI has correctly understood all requirements without verification
A developer shares their 4-month experience using Cursor Pro, detailing specific workflow optimizations and challenges. The post covers successful strategies like .cursorrules optimization, debug statement usage, and context management, while also highlighting limitations with less common technologies like Firebase/TypeScript, SwiftUI, and Svelte 5.
A user shares a valuable tip for improving code generation quality in Cursor AI by explicitly requesting it to ask clarifying questions. The post highlights how adding a simple prompt rule can prevent hallucinated code and lead to more accurate, contextually appropriate code generation through interactive refinement.
The post emphasizes the importance of using Git version control when working with Cursor AI to safely experiment with code changes. The author encourages developers to leverage Git's checkpoint system as a safety net, allowing them to explore different approaches and revert changes if the AI-generated code doesn't meet expectations.
The post shares a debugging methodology that emphasizes thorough problem analysis before jumping into code fixes. The approach recommends identifying 5-7 potential problem sources, narrowing them down to the most likely 1-2 causes, and validating assumptions through logging before implementing solutions.
A developer shares their frustrating experience with Cursor AI, where the majority of development time (5.5 out of 6 hours) was spent correcting the AI's mistakes and dealing with unresponsive behavior. The post highlights current limitations of AI-assisted coding tools and suggests they aren't yet mature enough for efficient development.