Stop building AI that acts like an oracle. Build a scientific partner instead.

I got a piece of feedback today that completely changed how I am architecting the AI features in my current project.

Like a lot of us building right now, I'm working on a tool that feeds application data to an LLM to generate plain-English insights. In my case, it’s a lightweight behavioral analytics tool that watches user drop-offs and tries to explain why they happened.

Initially, I thought the goal was to make the AI sound confident.

Data goes in ➔ AI spots a 10-second idle time on a checkout button ➔ AI tells the founder, "Your button contrast is too low."

But a developer in the community pointed out a massive flaw in this UX: It masks a hypothesis as a measured fact.