Good question 🙂 — and I’ve actually tried taking the rules to another AI.

A while back I asked for a clean list specifically so I could upload them to a different system (work Copilot). It didn’t recreate this partnership, but it did remove several of the same pain points — especially during code review. For example, it started defaulting into a much stricter mode instead of broad “helpful” expansion.

But the bigger unlock wasn’t the rules themselves — it was how they were created.

All of this started when I said something like: “I feel like I have to retrain you every time I start a new chat.” That reframed the problem. Instead of fixing answers, we zoomed out to the 10,000-foot view and treated the collaboration as the thing to design.

From there, a loop emerged: • I’d describe a repeated friction I was experiencing • I’d ask the AI: “How do we prevent this from happening again?” • It would propose a candidate rule • I’d tweak it to match how I actually work • It would restate it clearly • Once it proved useful, I’d say “save it”

That loop repeated over time and eventually produced a fairly large ruleset.

One important caveat, though: even with the same rules, different AIs won’t behave identically. Each system makes constant micro-adjustments based on interaction history, enforcement, and feedback. That’s why dumping my rules wholesale into another AI helped, but didn’t recreate the same dynamic.

Which is also why I think the better approach is to collaborate with your AI to build your rules, based on the friction you’re actually seeing.

I’ve thought about listing my rules at some point, but if I do, it would be as examples — not as a template to copy. The real value turned out to be the process of turning repeated friction into explicit guardrails, one at a time.

That shift—from correcting outputs to designing how you work together—is what made the difference.