Mirza Anwaarullah Baig

I keep seeing the same pattern across early-stage teams:

the MVP works until it really doesn t.

For many founders, the hardest part isn t getting something online
it s everything that comes after:
infra that cracks under real users
code that no dev wants to touch
rewriting the whole stack
AI-built projects no one can maintain
the moment you realize your prototype isn t a product

I keep seeing the same pattern across early-stage teams:

the MVP works until it really doesn t.

For many founders, the hardest part isn t getting something online
it s everything that comes after:
infra that cracks under real users
code that no dev wants to touch
rewriting the whole stack
AI-built projects no one can maintain
the moment you realize your prototype isn t a product

I keep running into the same pattern with AI coding tools: I type a quick starter prompt, get something that looks promising for a moment, and then, inevitably, it collapses into messy code and outputs I never wanted in the first place.

I ve seen this happen to others too. The tool isn t the problem. The problem is the prompt. Or rather, the lack of structure, clarity, and intention behind it.

So I m curious:

How do you plan your prompts when working with AI for code generation?
How much context and detail do you include up front?
Do you start small and iterate, or do you specify the entire mental model before generating anything?
What habits or prompting frameworks have actually helped you get clean, reliable code?