One post tagged with "cursor"

TLDR This article systematically reviews how to use AI coding assistants like Cursor efficiently and correctly. Key takeaways include: multi-turn conversations significantly reduce LLM accuracy—always prefer providing a complete, one-shot description of your requirements; optimizing your project structure and code semantics with an "AI-friendly architecture" (such as self-documenting code, contract-based design, structured READMEs, DSL term mapping, etc.) can greatly enhance AI understanding and collaboration; make full use of Cursor's various inquiry modes (Agent, Ask, Manual, Custom, Background) and context management features, flexibly switching according to your project's needs; and select the right AI model based on task type and model characteristics. The overarching message: treat code and documentation structure from an engineering perspective, proactively provide high-quality context for the AI, and you'll unlock the full value of AI coding assistants.