My Engineering Workflow in CursorAI
The article discusses how AI tools like Cursor have accelerated the typical software engineering workflow, reducing the time and effort required for each step.
Why it matters
This article highlights how AI is transforming the software engineering workflow, making developers more efficient and productive.
Key Points
- 1The traditional software engineering workflow involves steps like understanding requirements, designing, planning, implementing, reviewing, testing, and deploying
- 2With AI tools, the same workflow can now be executed much faster, reducing the time and context switching required
- 3The author uses Cursor and an 'ai-devkit' toolkit to streamline the workflow, automating tasks like requirement summarization, design review, and implementation
- 4AI helps the author stay in 'problem-solving mode' longer by handling repetitive tasks and providing assistance at each stage
Details
The article outlines the author's engineering workflow before and after incorporating AI tools. Traditionally, even mid-sized features would take weeks or months to complete due to the back-and-forth reviews, context switching, and waiting for feedback at each stage. However, with AI-powered tools like Cursor, the same workflow can now be executed much faster. The author uses Cursor commands and an 'ai-devkit' toolkit to automate tasks like requirement summarization, design review, and implementation. This allows the author to stay focused on problem-solving rather than getting bogged down in repetitive tasks. The article provides a step-by-step breakdown of how the AI-accelerated workflow looks, covering areas like feature development and understanding existing code.
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