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Coding is not just about asking AI for what you want.
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Ai generated, for entertainment purposes
Before you add AI to your product, find the screen where your users already copy and paste.
That's usually where the real AI feature lives. Not a chatbot in the corner, but a step people already do by hand: summarising a ticket, drafting a reply, tagging an upload, pulling fields out of a PDF.
Here's the order I follow when a founder asks me to add AI to an existing SaaS:
1. Pick one workflow, not "AI everywhere." Watch where users leave your app to paste something into ChatGPT and come back. That round trip is your feature.
2. Keep the model behind your own API. One service owns prompts, retries, logging and cost limits, so you can switch providers without touching the frontend.
3. Make it tenant-aware from day one. Each customer's data, prompts and usage stay scoped to their account, or the first enterprise security review will stop you.
4. Ship a draft, not a decision. Let the AI suggest and the user approve. You earn trust and get real feedback before you automate anything.
5. Measure accepted output, not tokens. If users rewrite most of what it produces, the prompt or the context is wrong, and that's your real roadmap.
Most AI features that flop didn't fail because of the model. They were bolted onto the wrong step.
What's the one task your users still copy into ChatGPT?
Watching where users paste into ChatGPT and come back is such a practical way to find the real feature. Most teams start with the chatbot instead.
Is it a dream? A trap? Or a new world with no way out? Watch my project "Red Door" — a story about choice, fear and meeting yourself, made with AI. Would you open the red door?
