From AI-Generated to Production-Ready: Closing the Gap
AI can write code fast, but getting it to production requires the same rigour as any other code — testing, review, observability, and ownership. Here's how to bridge that gap.
Published: Thursday, Apr 16, 2026 Last modified: Thursday, Apr 16, 2026
When creating a green field software project with AI, what makes that code “Production quality”?
From a “proof of concept”, instinctively I would:
- add tests (main_test.go) to capture the current tested functionality
- add a .github/workflow to ensure it builds and can be distributed
As a bonus I would add some sort of tagging / version release that is exhibited whilst running the app, e.g. v0.0.1
But more seriously
Feedback
A colleague suggested, how would you gather feedback that the product solved your users needs?
This is non-trivial, since you need adoption for feedback. Stars? Tweets? Email feedback? Telemetry? How do you capture and interpret this?
Continuous Deployment
How would you then improve on that product? If you host the software product on the Web, this is seamless, else you need to consider releasing the software as a package.