I keep running into AI features that are very good at getting to an answer, but a bit rubbish at showing how they got there. That feels fine until you need to explain the output, or catch the mistake before it ships.
Has anyone found a good balance between speed and being able to see what the tool actually did?
What stresses me out is when it gives you a clean “answer, ” but you can’t see what sources or files it actually touched.
“Hide their work” keeps them in the autocomplete bucket for me too, not the authority bucket. When there’s no sources, no intermediate steps, not even a clear list of assumptions, I treat the output as a sketch. I’ll use it for speed, but I won’t ship it until I can recreate the reasoning in my own words (or trace it back to something I’d cite).
Been there
Lol yeah, that “black box” vibe is exactly why I only treat it like autocomplete — when you say “been there, ” was it a case where it confidently gave you something wrong and you only caught it because you double-checked? honestly not sure on that bit.
Same here. The “black box” part isn’t even the scary bit for me — it’s that when it’s wrong, you don’t get any handles to debug it or even contest the decision.
Most folks seem aligned that “hidden work” tools are fine as accelerators but not as authorities. If you can’t see what sources/files it used, what assumptions it made, or any intermediate reasoning, then the output is basically a sketch: useful for speed, but not something you can ship or cite without independently recreating the logic and verifying the facts.
The unresolved caveat is that even when a tool does show steps, those steps can be post-hoc and still misleading, so transparency helps debugging but doesn’t magically make it trustworthy. Practical takeaway: treat opaque outputs as drafts, and build a workflow where the model must surface inputs, assumptions, and references (or you force it to), then you validate against primary sources/tests before it goes anywhere near production.
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