How do you decide when an AI feature is trustworthy enough to act on without you double-checking it?
I keep running into this building FounderFlow, and I don't think I've fully solved it.
Early on, our AI gave clean, confident-sounding answers for everything: "this deal is at risk," "reply to this today." It felt great in a demo. Then I used it daily across my own three businesses and caught it being confidently wrong more than once. That's a worse failure mode than just being vague, because you stop double-checking the exact moment it starts being wrong.
Our current fix is a confidence label on every insight (Verified, Very Likely, Needs Review, Monitor Only), so at least the AI is honest about what it actually knows. But that still leaves the human deciding how much to trust each tier, which is a judgment call, not a solved problem.
For anyone else shipping AI features that make real decisions or recommendations: how do you personally decide when something is safe to act on without a manual check? Is it based on the type of action, how long you've used the tool, a track record you've built up watching it be right, or something else entirely? Would love to hear how other founders think about this, especially if you've been burned before.
Oddapy
Personal life management. Privacy focused. Personal space.
Comments (10)
Hi Stacy,
I like the confidence-tier approach. One possible addition is to separate “how confident is the AI?” from “what is it allowed to do?”
The action policy could depend on risk and reversibility:
· Low-risk, easily reversible actions can run automatically.
· Medium-risk actions can be prepared as drafts for approval.
· High-impact or difficult-to-reverse actions should always require a manual check.
I would also reserve “Verified” for cases where the answer was checked against a trusted source, rule, or system of record, rather than relying on the model’s own confidence.
Then each action type can build its own track record over time. A system might earn more autonomy for drafting routine replies while still never receiving automatic authority over pricing, payments, contracts, or account changes.
So perhaps the key question is not only “Do I trust the AI?” but “What is the worst reasonable consequence if this specific action is wrong?”
Ayman
Hi Ayman, this is really helpful, thank you. Splitting "how confident is the AI" from "what is it allowed to do" is a good reframe, I've been conflating those two without realizing it. I like tying the action policy to risk and reversibility instead of just confidence level. Going to try mapping our action types against that reversibility question this week. And agreed on reserving Verified for checks against a real system of record, not just the model feeling sure. Appreciate you laying this out
I don't trust it entirely yet. When I am not qualified in the topic, I usually ask another LLM to double-check if the provided information is accurate. Include in my prompt a request to make a deep research and check the recent version of the official documentation and etc. So I take response from one LLM and ask another one to double-check it...
Sergey, cross checking one model against another is a smart workaround, especially for topics outside your own expertise. We do something similar internally before an insight ever gets shown to a user. Still, I think your instinct to keep a human in the loop on anything you're not qualified to judge yourself is the right call no matter how good these models get. Have you actually caught the two models disagreeing much, or has it mostly been confirmation so far?
Love this conversation because I struggled a lot with training AI to make the right decisions in my app too. What worked for me is having the AI state its confidence level for every result it produces, and then setting up rules for how it should behave at each level. Giving the user the ability to manually override the AI's decision also helps, and if the user overrides more than once it becomes a rule for the AI to follow going forward, like "in case of X always do Y." After struggling for a long time to get AI to make the right calls, I realized that combining probabilistic and deterministic approaches works best for me. Hope you find what works for your tool, Stacy!
Olga, combining probabilistic and deterministic rules, and letting repeated overrides turn into a standing rule, is a smart way to make the system earn trust instead of asking for it upfront. That override-to-rule idea is something I want to steal for FounderFlow, honestly. Right now confidence grading tells you what tier an insight sits in, but it does not yet learn from your corrections the way you are describing. Did that override-to-rule behavior take long to get right, or did it fall into place once you had the confidence tiers set up first?
↳ Replying to Stacy Wycoff
It took some trial and error! Confidence tiers came first. The override-to-rule part came after watching users keep correcting the same things. The hardest part was deciding how many corrections count as a pattern vs a one-time preference. Still refining it, but even a basic version made a big difference because the AI stopped repeating the same mistakes.
↳ Replying to Olga Kargopolova
Olga, deciding how many corrections count as a pattern versus a one time preference sounds like the harder half of the whole idea. Did you end up picking a fixed number of repeats before it became a rule, or does it depend on the type of correction?
↳ Replying to Stacy Wycoff
To provide the best experience for the user my latest rule is "Say it once. It remembers."
↳ Replying to Olga Kargopolova
Olga, "say it once, it remembers" might be the cleanest version of this idea I have heard so far. Simple enough to explain to a user in one sentence, but it forces the system to actually behave differently after a correction instead of just logging it somewhere. I am going to try stealing that exact framing when I explain override-to-rule behavior to our founding members.
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