This is a field-tested workflow for using AI effectively in production-grade, enterprise level React codebases in a monrepo setup — beyond toy Todo apps.
Everyone is using AI to boost development productivity these days and organization also pushing the developers to use more and more AI to increase productivity and efficiency. But after weeks of trial and error in a, I cab sat I I have sort of discovered that the real gains don't only come from better prompts or fancier models. They come from structured workflows.
This isn't a theoretical guide. It's my actual day-to-day workflow inside Visual Studio Code using GitHub Copilot (Enterprise version) on a production codebase with multiple apps, shared UI packages, and messy state flows. I use it for bug fixing, root cause analysis, feature addition, refactoring, and navigating codebases where "finding where this logic lives" used to take hours.
Here's how I made AI actually useful for real frontend engineering.
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