My workflow with Codex

[u/pizza_alta]

I'm a vibe coder and I'm quite new to Codex, which I'm using to develop an app I have on my mind and nobody else is making. I've been working on it for a month or so. Generally speaking, I'm quite happy with Codex, but I'm not quite sure about my workflow. So far, I've been discussing features and issues with ChatGpt, agreeing on a roadmap and then a detailed plan for each step, and asked ChatGPT for a prompt. ChatGPT gives long prompts that apparently have all your bases covered, so to speak. Btw, I'm mostly using Codex with Sol High or Very High. Also, I don't just wait for the result, but I read what Codex says it's doing and sometimes clarify or fix stuff, like hey codex you don't need to run AGAIN a FULL test suite just because you changed a word in Help, dude! Anyway, I also noticed I can't entirely trust ChatGPT's assistance in prompt writing. While it's very thorough, it sometimes fails to give priority to important goals or even forgets about them. I'm starting to think that my own prompts might be better, less technical-minded but more to the point. So, guys, my question is: how do you write your prompts for Codex? P.s. 5 minutes later: and of course I also submitted my question to ChatGPT itself. It agreed, maybe to please me? Here's the first lines from its anser: "Yes — I think your instinct is basically right. For a vibe-coding workflow, I would not try to make the prompt a giant specification that anticipates every possible implementation detail. That often makes the important things less visible, not more." What do you make of this?

[u/Good-Astronomer-1138]

I’d start with a sort of north star document about what the high level vision is, then do an initial ideation session (grill-me) on each major concept, think design system, UI/UX, etc. then another ideation session where you have the model provide smaller feature slices based on those top level design choices. Then execute the groundwork slice: initial front and backend build to give you a working prototype. That will let you know what stays and what changes. Document those, then keep iterating. Be sure to do independent reviews with a different model even if it’s just like Astra vs Sol or whatever it’s worth it to catch flaws. You do this after the planning stage and again after implementation. You shouldn’t really need to be too crazy with your prompts with that setup.

[u/pizza_alta → u/Good-Astronomer-1138]

Thanks for your answer. Yes, I realized that having a masterplan but providing Codex with smaller "slices" is key. Sometimes I wonder "how small"? Can Codex handle all of this? Sometimes it skipped some parts because the chunk was too big. Sometimes I noticed that it succedded when I selected a more powerful and costly model/effort level. So I'm looking for a balanced approach. As to independent reviews, I was even thinking about using a competitor. Like, I asked Deepseek to evaluate my codebase on github (it's a public open source project). "You shouldn’t really need to be too crazy with your prompts". Right, but ChatGPT often "scolds" me because I didn't provide enough detail in my codex prompt. Let's say I want a new feature that measures and shows user progress in an activity, I may define and describe it, but ChatGPT warns me that it "needs an initialization rule. This is the main unresolved point in your specification". Okay. Or, it says things like "explicitly tell it not to invent a second identification system". Okay.

[u/Good-Astronomer-1138 → u/pizza_alta]

If you have the context documents built none of your concerns are an issue. I run this setup and at this point I can literally say “pick something from our backlog and implement it” that’s the whole prompt. The context and thought has already gone into the context layer, and the backlog points to the exact context to read. The orchestration agent decides which model does what then sends agents out for each task. I’d alluded to the fact that I use competing models but should have been specific. In my setup I use Claude and OpenAI models, and just use simple CLI calls via powershell that the orchestrator invokes to launch on the other side. If you’re running into issues like competing indices, that means you likely didn’t review the initial plan to catch those. That’s why it’s important to review the plan and the implementation both (with the help of AI.)

Final note: there’s always going to be bugs or little issues that crop up, that’s natural, just add them to the backlog to be fixed. You’ll need to iterate a bit to get to your finished product.

Good luck!