The Problem

I was working on a Windows target where Claude simply would not run. This was not a degraded experience or a partial failure. It would not start at all due to a dependency issue. At the same time, the code I was building had to run in that environment, so avoiding the platform was not an option.

The obvious next step was to try to fix Claude on that system. I spent some time going down that path, but it quickly became clear that this was not going to be a quick fix. Even if I managed to get it working once, there was no guarantee it would continue working across updates or configuration changes. At that point, the problem started to look different.

In the previous post, I described how structured manifests and browser verification make execution deterministic.

That solves execution. It does not solve review.

Deterministic execution without deterministic review is incomplete.

In earlier posts, I described how I used ChatGPT for architectural reasoning and Claude Code for implementation. That workflow continues to evolve.

Recently I ran into two friction points that exposed a larger issue.

The problem was not capability. It was structure.

There’s a lot of hype right now around using AI in software development. And to be fair, the hype isn’t entirely misplaced. AI can write code. It can summarize, generate, and even refactor. But there’s a major problem with the way most people use it. The problem is guessing.

When AI Has to Guess

If your prompt is not sufficiently clear, the AI has no choice but to guess what you meant. And this guessing introduces subtle, and sometimes major, errors. It might guess wrong about your naming conventions, your error-handling preferences, your architectural style, or your end goals. Even if you think the prompt is clear, the AI may read it differently than you do.

In a recent software project, I took a new approach by using AI tools as collaborators in the development process. I didn’t use them for everything—but I did use them extensively in the areas where they shine: architecture reasoning, rapid prototyping, code refinement, and automated testing.

Rather than working full-time through the process, I engaged with ChatGPT in part-time conversations to shape the initial system architecture. Once the structure was clear, I used Claude Code to generate, refine, and test the implementation. It’s the first time I’ve worked this closely with two different LLMs across a single workflow, and the results were impressive. - The mentinon of Claude in this article is specifically about Claude Code and not Claude in general.