Previous in this series: Evaluating AI Tools, Part 1
Our original five-question checklist for evaluating AI output included one question worth a much closer look: can you explain the AI’s logic? Two separate AIPO cohorts independently gave us concrete, observable reasons why this deserves to be more than checklist item three of five – it may be one of the most practical, testable ways to tell tools apart.
Watching the Reasoning, Not Just the Answer
One cohort highlighted a specific tool feature worth knowing about: some AI tools now show their reasoning process as a visible trace before landing on a final answer, rather than just presenting a polished conclusion. Watching that progression matters – it lets you catch a wrong turn in the reasoning before it hardens into a confidently wrong final answer, rather than only being able to evaluate the finished product after the fact.
A Real Preference, Not Just a Feature
In a separate cohort, a participant explained a personal tool preference in almost identical terms: trusting one particular tool specifically because it explains its answers more clearly than the alternatives they’d tried. That’s worth taking seriously as a data point – when people across different contexts independently gravitate toward the tool that shows its work, that’s a real signal about what actually builds justified trust, not just an abstract ethics principle.
How to Actually Test for This
When you’re deciding between AI tools, don’t only judge them by whether you like their final answers – specifically test whether you can get a tool to explain its reasoning on request, and then evaluate that explanation critically. A tool that gives you a specific, traceable account of how it got somewhere (“I weighted X because you mentioned Y, and ruled out Z because of this constraint”) is fundamentally more auditable than one that gives you a vague, generic-sounding justification no matter what you ask it to explain.
This connects directly to spotting fabricated or hallucinated content: a model that’s actually reasoning from something real can usually explain where; one that’s confabulating tends to produce circular or evasive justification when pushed. “Explain your reasoning” isn’t just a trust-building habit – it’s one of your best tools for catching a confident wrong answer before you act on it.
Try This This Week
Take a tool you currently rely on and ask it to explain its reasoning behind a recent output – a recommendation, a summary, a prioritization call. Judge the explanation itself: is it specific enough that you could verify a piece of it independently, or is it generic enough that it could apply to almost any similar question? If you consistently get the vague version from one tool and the specific version from another, that’s a real, practical reason to weight your tool choice accordingly – not just a nice-to-have.
Keep This Handy
A practical, ready-to-use ethics checklist for Product Owners working with AI-generated outputs — covering bias, privacy, explainability, long-term impact, and human oversight, with concrete sub-steps and prompts for each, designed to be pulled out during refinement rather than filed away as a policy document.
Have a question, a use case you’re proud of, or a task you tried this on? We’d love to hear about it at the next Alumni Meetup — it’s one of the best places to trade real examples with other Product Owners working through the same challenges, and every session leaves you with at least one idea you didn’t walk in with.
A full, printable version of this checklist with additional prompt-level examples is available to AIPO class participants as part of the course materials. If you have taken an AIPO course with Rocket Nine Solutions and have misplaced the link to your Mural board with this information on it, please reach out via email at hello@rocketninesolutions.com.
Keep going:
- Ready to go deeper? Check out our upcoming AI courses for hands-on classes with real practice, not just reading.
- Want more like this? Subscribe to our YouTube channel for walkthroughs and demos that pair well with these posts.
- Have questions or want to talk it through? Reach us directly at hello@rocketninesolutions.com.
- Know a colleague wrestling with the same challenge? Send them this post — half the value of this series comes from the people who’ve already tried it sharing what worked.
A Note on How This Was Written: This post was developed collaboratively between Rocket Nine’s training team and Claude, Anthropic’s AI assistant — drawing on real material from our AI for Product Owners class and anonymized insights from the Product Owners who’ve taken it. We think that’s fitting: the same thoughtful human-AI collaboration we teach in class is exactly how we approached writing about it.
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