I kept fixing the same ChatGPT problem for months, so I built a system around the fix

For months I had the same experience with ChatGPT: some days it nailed exactly what I needed, other days the same kind of request came back generic and I'd end up rewriting it myself.

I assumed it was inconsistency in the model. It wasn't. When I actually compared my good prompts to my bad ones side by side, the good ones all shared the same structure — a clear role, specific context, a defined output format, explicit constraints. The bad ones were just a request with none of that, and the model was doing its best to guess the rest.

Once I started applying that structure deliberately, the difference stopped being occasional and became consistent.

That observation is what turned into OMNIMIND™ — a system built around this idea instead of a random prompt list: a library of 150+ prompts already structured this way across real use cases (career, business, marketing, content, etc.), a builder that generates a structured prompt from a plain-language goal when nothing in the library fits, and step-by-step workflows for goals a single prompt can't solve (like going from an idea to a launched product).

It works with ChatGPT, Claude and Gemini — same structure, any model.

Full transparency: this is a fresh launch, so I don't have sales numbers to share yet. What I can share is the actual reasoning behind it, which I think is useful on its own even if you never look at the product — role, context, format, constraints, applied to whatever you're prompting today.

If anyone wants to see how it's built: https://omnimind.vorneaux.com/

Genuinely curious what other builders here have figured out about getting consistent results from AI tools — is structure the main lever for you too, or have you found something else that matters more?