Developing a Repeatable AI Workflow to Reduce Mistakes
The author shares their approach to using AI effectively in daily development by establishing a structured, repeatable workflow to address common pain points like loss of context, inconsistent standards, and lack of documentation.
Why it matters
This approach demonstrates how establishing a repeatable, documentation-driven workflow can unlock the full potential of AI in daily development tasks.
Key Points
- 1Defined a multi-step workflow to separate planning, execution, and review
- 2Implemented a rule to read and update documentation before and after AI-assisted work
- 3Organized project structure to improve AI's understanding of the full system context
- 4Achieved fewer repeated mistakes, less rework, and more consistent output
Details
The author found that the main issue was not needing a smarter AI model, but rather establishing a repeatable process. They identified common pain points like AI losing context between sessions, breaking project standards, and treating documentation as an afterthought. To address this, they split the workflow into distinct phases - brainstorming, planning, optional quality checks, execution, review, and commit. The key change was a rule to have the AI read and update documentation before and after implementation. This helped the AI work with better project memory and context. The author also structured the project with separate folders for code and assets to further improve the AI's understanding of the full system. By implementing this structured workflow, the author was able to significantly reduce repeated mistakes, rework, and inconsistencies, while increasing overall productivity.
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