Stop Juggling AI Models

I think juggling different AI models inside a business is a complete waste of time.

That may get me some hate. Claude, ChatGPT, and Gemini all kind of look the same to me. The differences between them are less useful to my work than understanding what I am trying to accomplish and setting up the model to do it.

I have had good coding results with GPT-5.0 compared with ChatGPT 5.6, even through major revisions. My explanation is pretty simple: I understand the domain.

The work around the model matters

The model is only part of the setup. The structure around it matters too, including the rules you give it and the path you expect it to follow.

When that structure is loose, the model has more room to get lost while it tries to figure out what you want. When you remove unnecessary options and make the path clearer, there is less for it to sort through.

That does not mean adding a giant pile of instructions. More instructions can create more places to wander. The useful structure is the structure that keeps the work pointed at the actual job.

Domain knowledge helps because you can tell which rules matter. You know what the work is supposed to produce, where it tends to go sideways, and which constraints cannot be ignored. That lets you shape the task instead of hoping a newer model will figure out the whole situation for you.

Newer models still have a place

There is a fair exception here. If you are vibe coding, or using AI in a domain you do not know well, a newer model may be better at steering you away from tricky parts.

That is useful, but it is different from making constant model comparisons the centre of your AI work. A model may help you navigate unfamiliar ground. It still does not decide which business result deserves your attention.

Start with the business constraint

A better use of time is to start with the business itself:

1. Choose the metric you are trying to move.
2. Identify the constraint holding that metric back.
3. Work out the steps that could move you ahead.
4. Give the model the structure and rules it needs to stay on that path.

This keeps the conversation grounded in a specific piece of work that matters to the business and whether the setup helps move it.

Before switching models again, look at the task around the model. Tighten the rules, remove the unnecessary paths, and connect the work to a metric and a real constraint. That is usually the more useful place to spend your time.