u/Willing-Squash6929

A lot of GenAI workflows today still rely on:

prompt - output -refine - repeat

It works well for quick tasks, but when you try to build something larger, it can get inconsistent and hard to manage.

Recently I started experimenting with spec-driven development with GenAI.

Instead of prompting directly, I first define:

• what I want to build
• expected behavior
• inputs / outputs
• constraints and edge cases

Then I let the model generate based on that.

This small shift made a big difference:

• outputs are more consistent
• less back-and-forth refinement
• easier to debug and iterate

I’ve also been exploring tools that help track how AI applies these specs across a project like traycer, which makes things more manageable at scale.

Feels like spec-driven workflows could be a key layer for making GenAI more reliable beyond demos.

Curious if others here are experimenting with similar approaches.