> This article doesn't address writing code with AI, just code review. My issue with agentic coding is that I make numerous micro-architectural decisions while programming. I almost never have a full spec up front and develop one as I consider what I am writing.

working with AI forced me to write better specs but the way I write today is very different. I typically open Codex and have Linear MCP connected where my chat with the AI will end up writing the issue. Its a lot of back-end-forth where I tell what I want, the AI does all the code scanning, write something, I correct something, etc

The value for me is exactly that I tell what I want, the AI verify in the actual code if that's the path that makes more sense or not. In the end I have a pretty detailed spec that I'm much more confident is the correct path.

I find the spec easier to review than a huge PR so typically when executing is much faster and aligned with what I want.

The grill-me skill from Matt Pocock is great for this (https://github.com/mattpocock/skills/blob/main/skills/produc...)

> I fear that workflow is just chat, search, and being a rubber duck for my thoughts

This is exactly what I settled upon after my own trying really hard. It is liberating, I have no fear at all!

A lot of programming work is well represented in the training data. For that kind of stuff there’s not much to do regarding architectural decisions. I love to run the LLMs on auto for that work. But for anything not well represented in the training data, which could be anything from mundane stuff in PyQT or a truly novel application, keep them on a short leash or forget them altogether.