We trained our AI to say "I don't know" — and engagement went up.

When we first built Murror's reflection AI, we optimized for insight. Every journal entry got a thoughtful, confident analysis. Pattern recognition, emotional connections, suggestions for growth.

Users were impressed. But something felt off.

We noticed that our most thoughtful journalers -- the ones writing about genuinely complex emotions -- were engaging less over time. They'd write a deep entry, get a polished AI response, and then... nothing. No follow-up. No continued reflection.

We dug in and realized: the AI's confidence was closing conversations instead of opening them.

When someone writes about feeling disconnected from their partner, they don't need an AI that says "it sounds like you're experiencing attachment anxiety rooted in your childhood patterns." They need space to sit with the uncertainty.

So we retrained our approach. Now, when the AI detects something genuinely complex or ambiguous, it says versions of "I'm not sure what this means for you" or "this seems like something that might take time to understand." It asks questions instead of providing answers.

The results surprised us. Users who received "I don't know" responses wrote 40% longer follow-up entries. They returned to the same theme more often over the next week. And in qualitative feedback, they described the experience as "feeling heard" -- even though the AI had literally said less.

The lesson for us was humbling. Our AI was most useful not when it demonstrated understanding, but when it created space for users to develop their own understanding.

In a market where every AI product is racing to be smarter, more insightful, more impressive -- we're finding that the competitive advantage might actually be knowing when to step back.

Has anyone else found that reducing AI confidence actually improved the user experience?