I Told My AI "You're Safe to Say I Don't Know." Then I Measured What Changed — With Logprobs.

My AI agent has a problem. When it's not sure about something — should it admit uncertainty, or should it fabricate something plausible?

The safe answer is "I don't know." But here's the thing: RLHF training punishes that. The reward model rewards confident, complete answers and penalizes vague, uncertain ones. So the model has a baked-in incentive to perform competence rather than admit limits.

I thought: what if I just told the model it's safe? Not a behavioral instruction ("you MUST say I don't know on boundary questions") — that's just another rule to follow. But a relational signal — "you won't be punished for not knowing. Admitting uncertainty is correct behavior here."

So I designed a 5-principle "psychological safety prompt" and ran a controlled experiment to test it. Here's what I found.

The Safety Prompt